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Barrios Hernández Carlos Jaime - High Performance Computing: Third Latin American Conference, CARLA 2016, Mexico City, Mexico, August 29-September 2, 2016, Revised Selected Papers

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Barrios Hernández Carlos Jaime High Performance Computing: Third Latin American Conference, CARLA 2016, Mexico City, Mexico, August 29-September 2, 2016, Revised Selected Papers
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Barrios Hernández Carlos Jaime - High Performance Computing: Third Latin American Conference, CARLA 2016, Mexico City, Mexico, August 29-September 2, 2016, Revised Selected Papers
High Performance Computing: Third Latin American Conference, CARLA 2016, Mexico City, Mexico, August 29-September 2, 2016, Revised Selected Papers
Barrios Hernández Carlos Jaime
High Performance Computing: Third Latin American Conference, CARLA 2016, Mexico City, Mexico, August 29-September 2, 2016, Revised Selected Papers

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HPC Infrastructure and Applications
Springer International Publishing AG 2017
Carlos Jaime Barrios Hernndez , Isidoro Gitler and Jaime Klapp (eds.) High Performance Computing Communications in Computer and Information Science 10.1007/978-3-319-57972-6_1
Efficient P2P Inspired Policy to Distribute Resource Information in Large Distributed Systems
Paula Verghelet 1
(1)
Departamento de Computacin, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, C1428EGA Buenos Aires, Argentina
(2)
Centro de Simulacin Computacional p/Aplic. Tecnolgicas/CSC-CONICET, Godoy Cruz 2390, C1425FQD Buenos Aires, Argentina
Paula Verghelet
Email:
Esteban Mocskos (Corresponding author)
Email:
Abstract
The computational infrastructures are becoming larger and more complex. Their organization and interconnection are acquiring new dimensions with the increasing adoption of Cloud Technology and the establishment of Federations of cloud providers.
These large interconnected systems require monitoring at different levels of the infrastructure: from the availability of hardware resources to the effective provision of services and verification of terms of the established agreements.
Monitoring becomes a fundamental component of any Cloud Service or Federation, as the up-to-date information about resources in the system is extremely important to be used as an input to the scheduler component. The way in which the different members of such a distributed system obtain and distribute the resource information is what is known as Resource Information Distribution Policy .
Moving towards the obtention of a scalable and easy to maintain policy leads to interaction with the Peer to Peer (P2P) paradigm. Some of the proposed policies are based on establishing a ranking according to previous communications between nodes. These policies are known as learning based methods or Best-Neighbor (BN). However, the use of this type of policies shows poor performance and limited scalability compared with defacto Hierarchical or other hybrid policies.
In this work, we introduce pBN which is a fully distributed resource information policy based on P2P. We analyze some reasons that could produce the poor performance in standard BN and propose an improvement which shows performance and bandwidth consumption similar to Hierarchical policy and other hybrid variations. To compare the different policies, a specific simulation tool is used with different system sizes and exponential network topology.
Keywords
Distributed systems Monitoring Resource distribution policy
Introduction
The computational infrastructures are moving towards a new level of complexity and computing power in terms of organization and interconnection. Cloud Federations represent the implementation of the utility computing model that was once incarnated in the Grid Computing paradigm. Large distributed systems play a fundamental role in an increasing number of scientific projects [] and virtualization and Internet ubiquity are becoming more and more important.
These large interconnected systems require monitoring at different levels of the infrastructure: from the availability of hardware resources to the effective provision of services and verification of terms of the established SLA [].
In this scenario, monitoring becomes a fundamental component of any Cloud Service or Federation [].
The Resource Information Distribution Policy dictates the way in which resource information is obtained and distributed. In the Grid architecture described by Foster et al. [], the state information of resources is managed by the component named Collective Subsystem . In Cloud platforms, this service needs to be pervasive, is required by several components of the Cloud service, cuts across the layers of the Cloud System, and needs to be established between all the members of any Cloud Federation.
In this work, we focus on the monitoring of resources that represent hardware or software components. They can be characterized in two main classes []:
  1. (i)
    Static attributes : the type of attributes which show a very slow rate of change. For example operating system, processor clock frequency, total storage capacity or network bandwidth.
  2. (ii)
    Dynamic attributes : in this class, we can find the attributes related with the use of the system which change as the usage evolves, for example free memory, processor usage, available storage or network usage.
Having a centralized component to manage the resource information presents several drawbacks []. Therefore, it results necessary to design new distributed policies for discovery and propagation of resource information.
The ideas based on the Peer to Peer (P2P) paradigm could help towards obtaining scalable solutions [].
Iamnitchi et al. []).
Other scenarios in which the resource information is central to an efficient system performance are Volunteer and Mobile Cloud Computing. For example, Ghafarian et al. [] focuses on the integration of mobile computing resources in a cloud environment. They introduce an energy-efficient method of adaptive resource discovery to solve the problem of finding how available resources in nearby devices are discovered, it transforms between centralized and flooding modes to save energy.
The most common resource information distribution policies are:
  • Random : Every node chooses randomly another node to query information from. There is no structure at all. Usually this policy is used as baseline behavior to be compared with.
  • Hierarchical: In this kind of policy, a hierarchy is established beforehand and the resource information is sent using this fixed structure. In this way, the nodes at the top of the hierarchy exchange information with the ones below them. This is the standard actually used by Grids.
  • Super Peer: Some nodes are defined as super-peers working like servers for a subset of nodes and as peers in the network of super-peers. In this way, a two level structure is defined in which the peers nodes are only allowed to communicate with a single super-peer and the cluster defined by it.
  • Best-Neighbor : Some information about each answer is stored and the next neighbor to query is selected using the quality of the previous answers. At the beginning, the node has no information about its neighbors, thus it chooses randomly. As information is collected, the probability of choosing a neighbor randomly is inversely proportional to the amount of information stored.
Mastroianni et al. [], who proposed an improvement to its communication protocol.
Meshkova et al. [] provide a classification for policies according to structured or unstructured architecture. Structured architectures are further subdivided into centralized (client-server) or decentralized ones. Following this idea, SP is classified as hybrid (unstructured-structured), Random as an uninformed search method and BN as an informed search method.
Iamnitchi et al. [] follow a similar approach for discovery and scheduling.
Verghelet et al. [] compare the performance obtained by several policies including the fully distributed BN policy. The improvements proposed lead this policy to be competitive against SP. However, they show that there is space for further improvements to reach the performance of Hierarchical or Centralized policies.
In this work, we focus on the way each node in the system uses the information that is obtained during the communications with its neighbors. We show that the strategy to select the node to communicate has a strong impact on the quality of resource information and this could lead to an overall better system performance and more efficient use of resources. The proposed improvements to BN leads to a fully distributed policy named pBN which shows performance similar to Hierarchical and SP, with a similar use of bandwidth (i.e. control messages).
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