Load rebalancing
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The document discusses load rebalancing in distributed file systems for cloud computing. It notes that existing systems rely on central nodes for load balancing, which does not scale well. The proposed system aims to develop a fully distributed load rebalancing algorithm to address load imbalance among storage nodes in large, dynamic cloud environments consisting of thousands of nodes. The goal is to uniformly distribute file chunks among nodes while minimizing network traffic during rebalancing.
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![the file system [7], the file chunks are not distributed as uniformly as possible
among the nodes. Load balance among storage nodes is a critical function in
clouds.
Existing system:
State-of-the-art distributed file systems (e.g., Google GFS and Hadoop HDFS ) in
clouds rely on central nodes to manage the metadata information of the file
systems and to balance the loads of storage nodes based on that metadata. The
centralized approach simplifies the design and implementation of a distributed file
system.
Demerits of Existing system :
However, recent experience concludes that when the number of storage nodes, the
number of files and the number of accesses to files increase linearly, the central
nodes (e.g., the master in Google GFS) become a performance bottleneck, as they
are unable to accommodate a large number of file accesses due to clients and
MapReduce applications. Thus, depending on the central nodes to tackle the load
imbalance problem exacerbate their heavy loads.
Proposed system :
In this paper, we are interested in studying the load rebalancing problem in
distributed file systems specialized for large-scale, dynamic and data-intensive
clouds. (The terms “rebalance” and “balance” are interchangeable in this paper.)
Such a large-scale cloud has hundreds or thousands of nodes (and may reach tens
of thousands in the future). Our objective is to allocate the chunks of files as
uniformly as possible among the nodes such that no node manages an excessive
number of chunks. Additionally, we aim to reduce network traffic (or movement
cost) caused by rebalancing the loads of nodes as much as possible to maximize](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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considering the parameters that will affect the algorithm performance but also simplifying the structure of the network that will execute the algorithm. Our proposal was evaluated through mathematical analysis as well as computer simulations, and it was compared with the centralized approach and the original diffusion technique. Results show that our solution outperforms them in terms of throughput and response time.
Finally, we proved that our proposal converges to the state of equilibrium where the loads in all in-domain nodes are the same since each node receives an amount of load proportional to its capacity. Therefore, we conclude that this approach would have an advantage of being fair, simple and no node is privileged.
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Cloud computing is an upcoming era in software industry. It’s a very vast and developing technology.
Distributed file systems play an important role in cloud computing applications based on map reduce
techniques. While making use of distributed file systems for cloud computing, nodes serves computing and
storage functions at the same time. Given file is divided into small parts to use map reduce algorithms in
parallel. But the problem lies here since in cloud computing nodes may be added, deleted or modified any time
and also operations on files may be done dynamically. This causes the unequal load distribution of load among
the nodes which leads to load imbalance problem in distributed file system. Newly developed distributed file
system mostly depends upon central node for load distribution but this method is not helpful in large-scale and
where chances of failure are more. Use of central node for load distribution creates a problem of single point
dependency and chances of performance of bottleneck are more. As well as issues like movement cost and
network traffic caused due to migration of nodes and file chunks need to be resolved. So we are proposing
algorithm which will overcome all these problems and helps to achieve uniform load distribution efficiently. To
verify the feasibility and efficiency of our algorithm we will be using simulation setup and compare our
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system called KernelHive allows to efficiently run applications in a heterogeneous environment consisting
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system is open for optimizer implementations, focusing on various criteria. In this paper, we propose a new
optimizer for KernelHive, that utilizes distributed databases and performs data prefetching to optimize the
execution time of applications, which process large input data. Employing a versatile data management
scheme, which allows combining various distributed data providers, we propose using NoSQL databases
for our purposes. We support our solution with results of experiments with real executions of our OpenCL
implementation of a regular expression matching application in various hardware configurations.
Additionally, we propose a network-aware scheduling scheme for selecting hardware for the proposed
optimizer and present simulations that demonstrate its advantages.
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Load balancing is a computer networking method to distribute workload across multiple computers or a computer cluster, network links, central processing units, disk drives, or other resources, to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload. Using multiple components with load balancing, instead of a single component, may increase reliability through redundancy. The
load balancing service is usually provided by dedicated software or hardware, such as a multilayer switch or a Domain Name System server. In this paper, the existing static algorithms used for simple cloud load balancing have been identified and also a hybrid algorithm for developments in the future is suggested.
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Load balancing is used to distribute workloads across multiple servers in cloud computing. It aims to optimize resource use and minimize response time. The document proposes using a round robin approach to distribute loads from virtual machines across servers periodically to reduce server workload and use networks efficiently. Key benefits outlined are high scalability, availability, and flexibility to balance various protocols and route traffic based on server health. The conclusion states that load balancing is important in cloud computing to distribute work evenly for high user satisfaction and resource utilization, though further research is still needed.
Load rebalancing for distributed file systems in clouds
This paper addresses the load imbalance problem in distributed file systems used for cloud computing. Existing systems rely on central nodes for load balancing, which does not scale well. The paper proposes a fully distributed load rebalancing algorithm that operates without synchronization or global knowledge. It aims to uniformly distribute file chunks among nodes while minimizing network traffic during rebalancing. Simulation results show the proposed algorithm outperforms existing centralized and distributed approaches in terms of load imbalance, movement cost, and overhead.
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Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
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Distributed file systems play an important role in cloud computing applications based on map reduce
techniques. While making use of distributed file systems for cloud computing, nodes serves computing and
storage functions at the same time. Given file is divided into small parts to use map reduce algorithms in
parallel. But the problem lies here since in cloud computing nodes may be added, deleted or modified any time
and also operations on files may be done dynamically. This causes the unequal load distribution of load among
the nodes which leads to load imbalance problem in distributed file system. Newly developed distributed file
system mostly depends upon central node for load distribution but this method is not helpful in large-scale and
where chances of failure are more. Use of central node for load distribution creates a problem of single point
dependency and chances of performance of bottleneck are more. As well as issues like movement cost and
network traffic caused due to migration of nodes and file chunks need to be resolved. So we are proposing
algorithm which will overcome all these problems and helps to achieve uniform load distribution efficiently. To
verify the feasibility and efficiency of our algorithm we will be using simulation setup and compare our
algorithm with existing techniques for the factors like load imbalance factor, movement cost and network traffic.
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This is a presentation for Chapter 7 Distributed system management
Book: DISTRIBUTED COMPUTING , Sunita Mahajan & Seema Shah
Prepared by Students of Computer Science, Ain Shams University - Cairo - Egypt
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of multiple collections of nodes with different types of computing devices. The execution engine of the
system is open for optimizer implementations, focusing on various criteria. In this paper, we propose a new
optimizer for KernelHive, that utilizes distributed databases and performs data prefetching to optimize the
execution time of applications, which process large input data. Employing a versatile data management
scheme, which allows combining various distributed data providers, we propose using NoSQL databases
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implementation of a regular expression matching application in various hardware configurations.
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optimizer and present simulations that demonstrate its advantages.
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Load balancing is used to distribute workloads across multiple servers in cloud computing. It aims to optimize resource use and minimize response time. The document proposes using a round robin approach to distribute loads from virtual machines across servers periodically to reduce server workload and use networks efficiently. Key benefits outlined are high scalability, availability, and flexibility to balance various protocols and route traffic based on server health. The conclusion states that load balancing is important in cloud computing to distribute work evenly for high user satisfaction and resource utilization, though further research is still needed.
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An Efficient Decentralized Load Balancing Algorithm in Cloud Computing
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Load rebalancing for distributed file systems in clouds
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13
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Data-Intensive Technologies for Cloud Computing
This document provides an overview of data-intensive computing technologies for cloud computing. It discusses key concepts like data-parallelism and MapReduce architectures. It also summarizes several data-intensive computing systems including Google MapReduce, Hadoop, and LexisNexis HPCC. Hadoop is an open source implementation of MapReduce while HPCC provides distinct processing environments for batch and online query processing using its proprietary ECL programming language.
International Journal of Engineering Research and Development (IJERD)
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Reference from -:
MESOS >> A Platform for Fine-Grained Resource Sharing in the Data Center by Benjamin Hindman, Andy Konwinski, Matei Zaharia,Ali Ghodsi,Anthony D. Joseph, Randy Katz, Scott Shenker,Ion Stoica
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This document discusses energy-efficient strategies for cooperative multichannel MAC protocols. It introduces Distributed Information SHaring (DISH), which helps nodes make decisions by sharing information with neighboring nodes. This approach was shown to significantly increase throughput but had not addressed energy efficiency. The paper proposes two strategies: in-situ energy conscious DISH which uses existing nodes, and altruistic DISH which uses additional nodes called altruists. Evaluation shows altruistic DISH conserves 40-80% of energy, maintains throughput advantages, and more than doubles cost efficiency compared to protocols without this strategy. In-situ energy conscious DISH is only suitable in limited scenarios.
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IMPULSE TECHNOLOGIES,
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24
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IMPULSE TECHNOLOGIES,
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IMPULSE TECHNOLOGIES,
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22
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IMPULSE TECHNOLOGIES,
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IMPULSE TECHNOLOGIES,
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CTVS is a novel data extraction and alignment method that combines tag and value similarity to extract data from query result pages. It first identifies and segments query result records in the pages and aligns them into a table with data values from the same attribute in the same column. CTVS handles cases where records are not contiguous due to auxiliary information and any nested structures within records. It also designs a new record alignment algorithm that aligns attributes pairwise and holistically using tag and value similarity. Experimental results show CTVS achieves high precision and outperforms existing methods.
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The document discusses a new algorithm for topic mining over asynchronous text sequences. The algorithm aims to explore correlations between multiple related text sequences that may have different time stamps. It consists of two alternating steps: 1) extracting common topics from sequences based on adjusted time stamps, and 2) adjusting time stamps according to the discovered topic time distributions. The approach is evaluated on research papers and news articles, demonstrating effectiveness in identifying topics across asynchronously published documents.
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The document presents a new approach called TSCAN for temporally summarizing topics from a collection of documents. TSCAN first derives the major themes of a topic from the eigenvectors of a temporal block association matrix. It then extracts significant events and their summaries for each theme by examining the eigenvectors. Finally, it associates the extracted events based on their temporal closeness and context similarity to form an evolution graph of the topic. Experiments on the TDT4 corpus show that temporal summaries generated by TSCAN present topics in a comprehensible form and are superior to existing summarization methods based on human references.
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Load rebalancing
- 1. Load Rebalancing For Distributed File Systems in Clouds Abstract— Distributed file systems are key building blocks for cloud computing applications based on the Map Reduce programming paradigm. In such file systems, nodes simultaneously serve computing and storage functions; a file is partitioned into a number of chunks allocated in distinct nodes so that Map Reduce tasks can be performed in parallel over the nodes. However, in a cloud computing environment, failure is the norm, and nodes may be upgraded, replaced, and added in the system. Files can also be dynamically created, deleted, and appended. This results in load imbalance in a distributed file system; that is, the file chunks are not distributed as uniformly as possible among the nodes. Emerging distributed file systems in production systems strongly depend on a central node for chunk reallocation. This dependence is clearly inadequate in a large-scale, failure-prone environment because the central load balancer is put under considerable workload that is linearly scaled with the system size, and may thus become the performance bottleneck and the single point of failure. In this paper, a fully distributed load rebalancing algorithm is presented to cope with the load imbalance problem. Reasons for the proposal: Distributed file systems are key building blocks for cloud computing applications based on the Map Reduce programming paradigm. In such file systems, nodes simultaneously serve computing and storage functions; a file is partitioned into a number of chunks allocated in distinct nodes so that MapReduce tasks can be performed in parallel over the nodes. Because the files in a cloud can be arbitrarily created, deleted, and appended, and nodes can be upgraded, replaced and added in
- 2. the file system [7], the file chunks are not distributed as uniformly as possible among the nodes. Load balance among storage nodes is a critical function in clouds. Existing system: State-of-the-art distributed file systems (e.g., Google GFS and Hadoop HDFS ) in clouds rely on central nodes to manage the metadata information of the file systems and to balance the loads of storage nodes based on that metadata. The centralized approach simplifies the design and implementation of a distributed file system. Demerits of Existing system : However, recent experience concludes that when the number of storage nodes, the number of files and the number of accesses to files increase linearly, the central nodes (e.g., the master in Google GFS) become a performance bottleneck, as they are unable to accommodate a large number of file accesses due to clients and MapReduce applications. Thus, depending on the central nodes to tackle the load imbalance problem exacerbate their heavy loads. Proposed system : In this paper, we are interested in studying the load rebalancing problem in distributed file systems specialized for large-scale, dynamic and data-intensive clouds. (The terms “rebalance” and “balance” are interchangeable in this paper.) Such a large-scale cloud has hundreds or thousands of nodes (and may reach tens of thousands in the future). Our objective is to allocate the chunks of files as uniformly as possible among the nodes such that no node manages an excessive number of chunks. Additionally, we aim to reduce network traffic (or movement cost) caused by rebalancing the loads of nodes as much as possible to maximize
- 3. the network bandwidth available to normal applications. Moreover, as failure is the norm, nodes are newly added to sustain the overall system performance resulting in the heterogeneity of nodes.