Energy-Aware Mobile Service Overlays: Cooperative Dynamic Power Management in Distributed Mobile Systems

Seshasayee, Balasubramanian; Nathuji, Ripal; Schwan, Karsten

doi:10.1109/icac.2007.14

Cited by 22 publications

(12 citation statements)

References 26 publications

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“…Energy efficiency for communication system and computer network has recently become hot research area (Zhou et al 2004;Huang and Kao 2007;Chen and Leneutre 2008;Alsalih et al 2005;Shang et al 2002). Energy and deadline constrained scheduling have been studied in embedded system (Kumar et al 2007), cluster system (Seshasayee et al 2007;Zong and Qin 2007;Huang et al 2005;Kim et al 2005Kim et al , 2007Zhu et al 2005) and real-time system (Kumar et al 2007), but considering energy in grid computing is few.…”

Section: Related Work and Motivationsmentioning

confidence: 98%

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Collaboration among mobile agents for efficient energy allocation in mobile grid

2011

Inf Syst Front

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The use of mobile devices in grid environments may have two interaction aspects: devices are considered as users of grid resources or as grid resources providers. Due to the limitation constraints on energy and processing capacity of mobile devices, their integration into the Grid is difficult. In this paper, we investigate the cooperation among mobile devices to balance the energy consumption and computation workloads. Mobile devices can have different roles such as buyer devices and seller devices. In the mobile grid, the energies of mobile devices are uneven, energy-poor devices can exploit other devices with spare energy. Our model consists of two actors: A buyer device agent represents the benefits of mobile buyer device that intends to purchase energy from other devices. A seller device agent represents the profits of mobile seller device that is willing to sell spare energy to other devices. The objective of optimal energy allocation in mobile grid is to maximize the utility of the system without exceeding the energy capacity, expense budget and the deadline. A collaboration algorithm among mobile agents for efficient energy allocation is proposed. In the simulation, the performance evaluation of collaboration algorithm among mobile agents is conducted.

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Section: Related Work and Motivationsmentioning

confidence: 98%

“…Balasubramanian Seshasayee et al (Seshasayee et al 2007) discuss the problem of cooperative energy management in distributed mobile systems. It presents decentralized management techniques when running a distributed application in a MANET environment.…”

Section: Related Work and Motivationsmentioning

confidence: 98%

Collaboration among mobile agents for efficient energy allocation in mobile grid

2011

Inf Syst Front

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“…These approaches address either general-purpose service placement directly [34,58] or as part of placing the components of a hierarchical service overlay [98].…”

Section: Discussionmentioning

confidence: 99%

“…As part of their Mobile Service Overlays project [98,99], Seshasayee et al propose a method for distributing the components of a service across the nodes of a MANET. The aim of this project is to use dynamic load balancing as a mean to save energy and ultimately prolong the lifetime of the network.…”

Section: Service Chain Placement In Manetsmentioning

confidence: 99%

Service Placement in Ad Hoc Networks

Wittenburg¹,

Schiller²

2012

SpringerBriefs in Computer Science

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Service provisioning in ad hoc networks is challenging given the difficulties of communicating over a wireless channel and the potential heterogeneity and mobility of the devices that form the network. In order to optimize the performance of the network over which a service host provides a service to client nodes, it is necessary to continuously adapt the logical network topology to both external (e.g., wireless connectivity, mobility, churn) and internal (e.g., communication patterns, service demand) factors. Recent proposals advocate that nodes should dynamically choose which nodes in the network are to provide application-level services to other nodes. Services in this context range from infrastructural services such as the Domain Name System (DNS) to user-oriented services such as the World Wide Web (WWW).Service placement is the process of selecting an optimal set of nodes to host the implementation of a service in light of a given service demand and network topology. The main questions addressed by service placement are: How many instances of the same service should be available in the network and cooperate to process clients' service requests; where these service instances should be placed, i.e., which nodes are best suited for hosting them;and when to adapt the current service configuration. The service instances of a distributively operating service are exact copies of the software component that provides the service, including both the executable binary and the application-level data. The set of nodes that host a service instance is referred to as the service configuration. A good service configuration increases the performance of a service according to application-specific quality metrics, while at the same time potentially reducing the overall network load. The key advantage of active service placement in ad hoc networks is that it allows for the service configuration to be adapted continuously at run time.In this work, we propose the SPi service placement framework as a novel approach to service placement in ad hoc networks. The SPi framework takes advantage of the interdependencies between service placement, service discovery and the routing of service requests to minimize signaling overhead. We also propose the Graph Cost / Single Instance (GCSI) and the Graph Cost / Multiple Instances (GCMI) placement algorithms. The SPi framework employs these algorithms to optimize the number and the location of service instances based on usage statistics and a partial network topology derived from routing information. The GCSI and GCMI placement algorithms only require minimal knowledge about the service they are tasked with placing in the network. They are novel in that they take the communication between service instances into account which is required to synchronize the global state of the service. Furthermore, when calculating the optimal timing of their placement decisions, the two algorithms explicitly consider the overhead of the actions required for implementing changes to the current service c...

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“…Since 2000s onward, more works on policies and decision algorithms started to appear. The proposed algorithms aim at managing or adapting the execution of the mobile applications, on the basis of three different approaches: program partitioning [14], [28], [47], [48], [56], [67], [69], [76], [78], energy-aware execution [63], [65] and context-awareness [35], [37], [38], [72]. A policy for task offloading decision is typically driven by objectives of performance improvement, i.e., meeting response time requirements, meeting real-time constraints, etc.…”

Section: Computation Offloading For Mobile Systemsmentioning

confidence: 99%

Towards Distributed Mobile Computing

Massari

Zanella

Fornaciari

2016

2016 Mobile System Technologies Workshop (MST)

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Abstract-In the latest years, we observed an exponential growth of the market of the mobile devices. In this scenario, it assumes a particular relevance the rate at which mobile devices are replaced. According to the International Telecommunicaton Union in fact, smart-phone owners replace their device every 20 months, on average. The side effect of this trend is to deal with the disposal of an increasing amount of electronic devices which, in many cases, are still working. We believe that it is feasible to recover such an unexploited computational power. Through a change of paradigm in fact, it is possible to achieve a two-fold objective: 1) extend the mobile devices lifetime; 2) enable a new opportunity to speed up mobile applications. In this paper we aim at providing a survey of state-of-art solutions aim at going in the direction of a Distributed Mobile Computing paradigm. We put in evidence the challenges to be addressed in order to implement this paradigm and we propose some possible future improvements.

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Energy-Aware Mobile Service Overlays: Cooperative Dynamic Power Management in Distributed Mobile Systems

Cited by 22 publications

References 26 publications

Collaboration among mobile agents for efficient energy allocation in mobile grid

Collaboration among mobile agents for efficient energy allocation in mobile grid

Service Placement in Ad Hoc Networks

Towards Distributed Mobile Computing

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