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represents simulation results of Mean Square Error (MSE) and probability of correct execution respectively, for adjustment of the number of replication nodes where learning rate l = 0.1 and credibility threshold thr = 0.637.
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Although desktop Grid computing has been regarded as a cost-efficient computing paradigm, the system has suffered from scalability issues caused by its centralized structure. In addition, resource volatility generates system instability and performance deterioration. However, regarding the provision of a reliable and stable execution environment, r...
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Distributed and collaborative applications are rapidly converging towards the adoption of a computing paradigm based on service-oriented architectures, according to which an application results from the composition of a set of services in execution on networked server hosts. In this context, a major challenge for collaborative businesses and applic...
One of the challenges a scientific computing center has to face is to keep delivering
well consolidated computational frameworks (i.e. the batch computing farm), while conforming to modern computing paradigms. The aim is to ease system administration at all levels (from hardware to applications) and to provide a smooth end-user experience. Within t...
Volunteer computing has come up as a new form of distributed computing. Unlike other computing paradigms like Grids, which use to be based on complex architectures, volunteer computing has demonstrated a great ability to integrate dispersed, heterogeneous computing resources with ease. This article presents ZIVIS, a project which aims to deploy a c...
Citations
... Some of the popular volunteer computing systems are BOINC [8,9], condor-like grid system [10], Entropia [11], XtremeWeb [12], Aneka [13], and SZTAKI [14]. Peer-to-Peer (P2P) based volunteer computing (VC) systems represent a decentralized, self-organized and scalable environment for running applications such as PastryGrid [15], BonjourGrid [16], ShareGrid [17], Condor-Flock P2P [18], and Self-Gridron [19]. A fundamental challenge in this large, decentralized and distributed resource sharing environment is efficient discovery of * Corresponding author. ...
Volunteer computing which benefits from idle cycles of volunteer resources over the Internet can integrate the power of hundreds to thousands of resources to achieve high computing power. In such an environment the resources are heterogeneous in terms of CPU speed, RAM, disk capacity, and network bandwidth. So finding a suitable resource to run a particular job becomes difficult. Resource discovery architecture is a key factor for overall performance of peer-to-peer based volunteer computing systems. The main contribution of this paper is to develop a proximity-aware resource discovery architecture for peer-to-peer based volunteer computing systems. The proposed resource discovery algorithm consists of two stages. In the first stage, it selects resources based on the requested quality of service and current load of peers. In the second stage, a resource with higher priority to communication delay is selected among the discovered resources. Communication delay between two peers is computed by a network model based on queuing theory, taking into account the background traffic of the Internet. Simulation results show that the proposed resource discovery algorithm improves the response time of user’s requests by a factor of 4.04 under a moderate load.
... Consequently, fully-centralized P2P Desktop Grid allowing each autonomic desktop computer to individually allocate resources as a scheduler has become a promising trend. It is no wonder that there already emerged quite a few corresponding projects, such as PastryGrid [3], BonjourGrid [4], Condor-Flock P2P [5], Self-Gridron [6], [7], etc. ...
Fully decentralized resource allocation for P2P desktop Grid allows each participating node to act as both resource provider and requester. The system performance indicators (including throughput, makespan, etc) are easily degraded by the unbalanced load distribution, which is probably caused by the fast-changing states of heterogeneous resources due to arbitrary task submissions. Although the cooperative load rebalancing methods can mitigate the problem, they are likely to introduce the contention on under-utilized resources with growing task arrival rates, leading to the sub-optimal load balancing efficacy. Our focus is on how to optimize load balancing status by taking into account minimizing the conflict of autonomic task migration decisions in P2P desktop Grid. Our load rebalancing process is modeled as a set of independent stochastic Bernoulli trials by letting each heavily loaded node push its surplus loads to its surrounding lightly loaded nodes. We proved that the surplus load amount should be shifted based on a proper ratio by considering decision conflicts and designed a novel load balancing algorithm with provably small decision conflict probability. We derived an upper-bound for this probability, which will be reduced down to about 2% under our algorithm. Finally, we validated via simulation that the system performance can be significantly improved accordingly.
Volunteer computing systems exploiting large amounts of geographically dispersed
resources on the Internet for solving complex scientific problems. However, scheduling
scientific workflows in a fully decentralised way and low overhead is a challenging task
in these environments. To counter this challenge, this paper presents a fully
decentralised proximity-aware workflow-scheduling policy for these environments.
The proposed scheduling consists of three phases. In the first phase, each workflow
application is partitioned into sub-workflows in order to minimise data dependencies
among them. The second phase of the workflow-scheduling algorithm finds some
resources to execute each sub-workflow. These resources are selected based on Quality
of Service (QoS) constraints of the workflow, load balancing and proximity of
resources. Each workflow can have QoS constraints in terms of minimum CPU speed
and minimum RAM or hard disk requirements. In the third phase, sub-workflows will
be executed on each resource based on local scheduling algorithm to minimise the
partial makespan. The proposed scheduling policy focuses on the reduction of
communication overhead to improve the performance of I/O-intensive and dataintensive
workflows. Simulation results show that the proposed workflow-scheduling
policy improves the average response time of scientific workflows up to 53.6% under a
moderate load.
One of the main challenges in peer-to-peer-based volunteer computing systems is an efficient resource discovery algorithm. Load balancing is a part of resource discovery algorithm and aims to minimize the overall response time of the system. This paper introduces an analytical model based on distributed parallel queues to optimize the average response time of the system in a distributed manner. The proposed resource discovery algorithm consists of two phases. In the first phase, it selects peers in a load-balanced manner based on QoS constraints of request. In the second phase, a proximity-aware feature is applied to select the peer with minimum communication overhead among selected peers in the first phase. Two dispatching strategies are proposed for the load balancing based on stochastic analysis of routing in the distributed parallel queues. These policies adopt probabilistic and deterministic sequences to redirect requests to the capable peers in the system. Simulation results show that the proposed resource discovery algorithm improves the response time of user’s requests by a factor of 1.8 under a moderate load.
Peer-to-peer Desktop Grids provide integrated computational resources by leveraging autonomous desktop computers located at the edge of the Internet to offer high computing power. The arbitrary arrival and serving rates of tasks on peers impedes the high throughput in large-scale P2P Grids. We propose a novel autonomous resource allocation scheme, which can maximize the throughput of self-organizing P2P Grid systems. Our design possesses three key features: (1) high adaptability to dynamic environment by proactive and convex-optimal estimation of nodes’ volatile states; (2) minimized task migration conflict probability (upper bound can be limited to 2%) of over-utilized nodes individually shifting surplus loads; (3) a load-status conscious gossip protocol for optimizing distributed resource discovery effect. Based on a real-life user’s workload and capacity distribution, the simulation results show that our approach could get significantly improved throughput with 23.6–47.1% reduction on unprocessed workload compared to other methods. We also observe high scalability of our solution under dynamic peer-churning situations.
