This paper proposes a cloud-integrated sensor networking architecture, called Sensor-Cloud Integration Platform as a Service (SC-iPaaS), which hosts virtualized sensors in clouds and operates physical sensors through their virtual counterparts. SC-iPaaS performs push-pull hybrid communication between three layers: cloud, edge and sensor layers. This paper formulates an optimization problem for SC-iPaaS to seek the optimal data transmission rates for individual nodes and examines evolutionary optimization with respect to multiple conflicting objectives subject to given constraints. Simulation results show that multiobjective analysis is critical in configuring and operating three-tier push-pull hybrid communication in SC-iPaaS.
This paper proposes and evaluates a multiobjective evolutionary game theoretic framework for adaptive and stable application deployment in clouds that support dynamic voltage and frequency scaling (DVFS) for CPUs. The proposed framework, called Cielo, aids cloud operators to adapt the resource allocation to applications and their locations according to the operational conditions in a cloud (e.g., workload and resource availability) with respect to multiple conflicting objectives such as response time performance, recourse utilization and power consumption. Moreover, Cielo theoretically guarantees that each application performs an evolutionarily stable deployment strategy, which is an equilibrium solution under given operational conditions. Simulation results verify this theoretical analysis; applications seek equilibria to perform adaptive and evolutionarily stable deployment strategies. Cielo allows applications to successfully leverage DVFS to balance their response time performance, resource utilization and power consumption.
Abstract-This paper studies an evolutionary game theoretic framework for adaptive and stable application deployment in clouds. The framework, called Cielo, aids cloud operators to adapt the resource allocation to applications and their locations to the operational conditions in a cloud (e.g., workload and resource availability) with respect to multiple conflicting objectives (e.g., response time and power consumption). Moreover, Cielo theoretically guarantees that each application performs an evolutionarily stable deployment strategy, which is an equilibrium solution under given operational conditions. Simulation results verify this theoretical analysis; applications seek equilibria to perform adaptive and evolutionarily stable deployment strategies. Cielo outperforms well-known existing heuristics.
Abstract-This paper focuses on push-pull hybrid communication in a cloud-integrated sensor networking architecture, called Sensor-Cloud Integration Platform as a Service (SC-iPaaS). SCiPaaS consists of three layers: sensor, edge and cloud layers. The sensor layer consists of wireless body sensor networks, each of which operates several sensors for a homebound patient for a remote physiological and activity monitoring. The edge layer consists of sink nodes that collect sensor data from sensor nodes in the sensor layer. The cloud layer hosts cloud applications that obtain sensor data through sink nodes in the edge layer. This paper formulates an optimization problem for SC-iPaaS to seek the optimal data transmission rates for individual sensor and edge nodes and solves the problem with respect to multiple objectives (e.g., data yield, bandwidth consumption and energy consumption) subject to given constraints. This paper sets up a simulation environment that performs remote multi-patient monitoring with five on-body sensors including ECG, pulse oximeter and accelerometer per a patient. Simulation results demonstrate that the proposed optimizer successfully seeks Pareto-optimal data transmission rates for sensor/sink nodes against data request patterns placed by cloud applications. The results also confirm that the proposed optimizer outperforms an existing well-known optimization algorithm.
This paper considers a neighborhood broadcasting protocol in undirected de Bruijn and Kautz networks. The neighborhood broadcasting problem(NBP) is the problem of disseminating a message from an originator vertex to only its neighbors. Our protocol works under the single-port and half-duplex model and solves NBP in 5 log 2 (n + 1) + O(1) time units on the undirected de Bruijn graph UB(n, d) with n d vertices and the undirected Kautz graph UK(n, d) with n d +n d−1 vertices, where 2n is the maximum degree of these graphs. This completion time is asymptotically optimal in this model.
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