An Intelligent Task Allocation Scheme for Multihop Wireless Networks

Jin, Yichao; Jin, Jiong; Gluhak, Alexander; Moessner, Klaus; Palaniswami, Marimuthu

doi:10.1109/tpds.2011.172

Cited by 70 publications

(50 citation statements)

References 19 publications

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“…There has also been some work considering energy and delay sensitive scheduling and partitioning of tasks in collaborative networks [20]- [24]. However, the tradeoffs considered in these works is quite different from ours.…”

Section: Related Workmentioning

confidence: 93%

Network-Assisted Mobile Computing with Optimal Uplink Query Processing

Chan¹,

Bambos²,

Singh³

2013

IEEE Trans. on Mobile Comput.

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Abstract-Many mobile applications retrieve content from remote servers via user generated queries. Processing these queries is often needed before the desired content can be identified. Processing the request on the mobile devices can quickly sap the limited battery resources. Conversely, processing user-queries at remote servers can have slow response times due communication latency incurred during transmission of the potentially large query. We evaluate a network-assisted mobile computing scenario where midnetwork nodes with "leasing" capabilities are deployed by a service provider. Leasing computation power can reduce battery usage on the mobile devices and improve response times. However, borrowing processing power from mid-network nodes comes at a leasing cost which must be accounted for when making the decision of where processing should occur. We study the tradeoff between battery usage, processing and transmission latency, and mid-network leasing. We use the dynamic programming framework to solve for the optimal processing policies that suggest the amount of processing to be done at each mid-network node in order to minimize the processing and communication latency and processing costs. Through numerical studies, we examine the properties of the optimal processing policy and the core tradeoffs in such systems.

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Section: Related Workmentioning

confidence: 93%

Network-Assisted Mobile Computing with Optimal Uplink Query Processing

Chan¹,

Bambos²,

Singh³

2013

IEEE Trans. on Mobile Comput.

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“…In the proposed algorithm, the construction process of discrete particle swarm optimization (DPSO) is achieved through adopting a binary matrix encoding form, minimizing tasks execution time, saving node energy cost, balancing network load, and defining a fitness function for improving scheduling effectiveness and system reliability. Jin et al [5] proposed an adaptive intelligent task mapping together with a scheduling scheme based on a genetic algorithm (ITAS). They employed a hybrid fitness function in the algorithm to extend the overall network lifetime via workload balancing among collaborative nodes.…”

Section: IImentioning

confidence: 99%

“…Zeng et al [6] developed an energy balanced directed acyclic graph (DAG) task scheduling algorithm and gave a genetic algorithm (GA) integrating chromosome coding to find approximate optimal solution. Both [5] and [6] adopt GA, but GA may easily get stuck in local optimum. Satish Penmatsa [7] present a game theoretic approach to solve the static load balancing problem for single class and multi-class (multi-user) jobs in a distributed system where the computers are connected by a communication network.…”

Section: IImentioning

confidence: 99%

QOS Improved Load Balancing Framework for Wireless Networks

Rajguru¹

2017

IJRASET

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Quality-of-service (QoS) is very important issues in load balancing and task scheduling process in distributed network system. The non-efficient load balancing and task scheduling algorithm decreases Quality-of-service (QoS) parameters like schedule delay, energy consumption, throughput etc. In this research paper, we proposed QoS improved load balancing frameworks for wireless networks (WNs). We improve QoS of distributed frameworks by using particle swarm optimization and cuckoo search method. The proposed technique improves QoS, such as energy consumption, latency, throughput, network life time. Keywords: wireless networks (WNs), load balancing, task scheduling, particle swarm optimization (PSO), cuckoo search (CS) I.INTRODUCTION A Distributed Network System can be viewed as a collection of computing and communication resources shared by active users [2][3]. The distributed network system can be classified into cloud computing, cluster computing, P2P system, grid computing etc. In distributed network systems the management of resources and applications is a very difficult task [1]. Resource sharing is the most important features of distributed system. The applications of distributed networks are internet, telecommunication networks and computer networks etc. When the demand for computing power increases, the load balancing and task scheduling problems becomes very important. The load balancing and task scheduling in distributed network system are most challenging research area in computer science and engineering. Load balancing and task scheduling in distributed network system has an important role in overall quality of service (QoS). Task scheduling in distributed system can be defined as allocating processes to processor so that total execution time will be minimized, utilization of processors will be optimized. Load balancing is the process of improving the performance of system through a redistribution of load among processor. In this research paper, we proposed QoS improved load balancing frameworks for wireless networks (WNs). We improve QoS of distributed frameworks by using particle swarm optimization and cuckoo search method. The proposed technique improves QoS, such as energy consumption, latency, throughput, network life time.

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“…To alleviate this limitation, we aim at using a mobile cloud to facilitate light-weight provisioning of complex mobile Web services through service fragment and distribution, thus reducing the individual MHs' energy usage and increasing the range and complexity of services that can be executed/provided on MHs. Although, distributing mobile applications is not a new concept and has been previously used for application distribution and load balancing [12,13], but it has not been used for offloading the execution tasks of mobile Web services to run remotely on other mobile devices. The next section highlights the motivation towards mobile cloud; it also, presents the mechanisms that are used for service distribution.…”

Section: Background and Related Workmentioning

confidence: 99%

Mobile Cloud Computing for Providing Complex Mobile Web Services

AlShahwan

Faisal

2014

2014 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering

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Abstract-Simple Web services can be provided directly from one mobile device acting as a server. However, complex services need a mobile cloud to provide computing resources and infrastructure to support seamless provision of its Web services in a light weight manner. Our approach focuses on the architecture of this mobile cloud, which consists of a set of collaborative mobile devices that relies on an Extended Mobile Host Complex Web service Framework (EMHCWF). The main building blocks of EMHCWF with their associated distributed mechanisms are defined and tested experimentally. Furthermore, the safety properties of mobile cloud infrastructure have been validated using formal methods that are based on process algebra.

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An Intelligent Task Allocation Scheme for Multihop Wireless Networks

Cited by 70 publications

References 19 publications

Network-Assisted Mobile Computing with Optimal Uplink Query Processing

Network-Assisted Mobile Computing with Optimal Uplink Query Processing

QOS Improved Load Balancing Framework for Wireless Networks

Mobile Cloud Computing for Providing Complex Mobile Web Services

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