Search Results for « Task Allocation »

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

Dynamic task allocation for unmanned aerial vehicles (UAVs) is often constrained by stale and asymmetric information. Variants of the Consensus-Based Bundle Algorithm (CBBA) can waste bandwidth by transmitting every state or bid update without distinguishing assignment-altering information from irrelevant perturbations. This paper proposes Assignment-Consistent Dynamic Multi-UAV Task Allocation (AC-DMTA), a decision-centric framework in which agents communicate only when local uncertainty may invalidate the current assignment. AC-DMTA represents stale records with uncertainty sets and converts point bids into interval bids, enabling task-winner and bundle-stability certificates to suppress assignment-irrelevant messages. These certificates provide local sufficient conditions for certifying the stability of task-winner and bundle decisions within the modeled uncertainty envelopes and candidate sets, allowing assignment-irrelevant communication to be skipped. Timestamp and recency records further resolve asynchronous conflicts without broad resynchronization. Across four benchmark environments and 20 paired seeds, AC-DMTA sends fewer bytes and directed messages than periodic CBBA, Event-Driven Consensus-Based Bundle Algorithm (ED-CBBA), Clustered CBBA, and Two-Level Clustered Consensus-Based Bundle Algorithm (TLC-CBBA), achieving byte reductions of 10.4–91.1% across baseline–scenario pairs while maintaining competitive reward and task-completion rates. Under the tested delay and packet-loss stress conditions, AC-DMTA also achieves the lowest number of bytes per completed task among the compared methods.