Learning automata based decision making algorithm for task offloading in mobile cloud
06 Jul 2016-pp 1-6
TL;DR: A model for task offloading using learning automata based decision making algorithm (LADMA) is proposed that considers the completion time and energy consumption of the tasks during the allocation ofThe tasks to the suitable VMs in the cloud.
Abstract: In recent years, mobile cloud computing (MCC) is treated as one of the important enablers of Internet of Things. MCC has evolved from a mix of mobile computing and cloud computing. Most of the researcher works on MCC focus on the reduction of cost of applications in mobile devices by leveraging cloud technology. The limitations of mobile environment such as computation capacity, battery power and limited memory lead to the integration of cloud technology. The performance of the mobile environment improves by the implementation of task offloading using cloud technology. In this paper, a model for task offloading using learning automata based decision making algorithm (LADMA) is proposed. The algorithm considers the completion time and energy consumption of the tasks during the allocation of the tasks to the suitable VMs in the cloud. The proposed model is tested on Amazon EC2 and Android X 86 Platforms.
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TL;DR: A context-aware mixed integer programming model is proposed to provide off-line optimal solutions for making the offloading decisions and scheduling the offloaded tasks among the shared computing resources in heterogeneous mobile clouds.
Abstract: Mobile cloud computing is emerging as a promising approach to enrich user experiences at the mobile device end. Computation offloading in a heterogeneous mobile cloud environment has recently drawn increasing attention in research. The computation offloading decision making and tasks scheduling among heterogeneous shared resources in mobile clouds are becoming challenging problems in terms of providing global optimal task response time and energy efficiency. In this article, we address these two problems together in a heterogeneous mobile cloud environment as an optimization problem. Different from conventional distributed computing system scheduling problems, our joint offloading and scheduling optimization problem considers unique contexts of mobile clouds such as wireless network connections and mobile device mobility, which makes the problem more complex. We propose a context-aware mixed integer programming model to provide off-line optimal solutions for making the offloading decisions and scheduling the offloaded tasks among the shared computing resources in heterogeneous mobile clouds. The objective is to minimize the global task completion time (i.e., makespan). To solve the problem in real time, we further propose a deterministic online algorithm—the Online Code Offloading and Scheduling (OCOS) algorithm—based on the rent/buy problem and prove the algorithm is 2-competitive. Performance evaluation results show that the OCOS algorithm can generate schedules that have around two times shorter makespan than conventional independent task scheduling algorithms. Also, it can save around 30% more on makespan of task execution schedules than conventional offloading strategies, and scales well as the number of users grows.
62 citations
Cites methods from "Learning automata based decision ma..."
...[Krishna et al. 2016] proposed a model for task offloading using learning automata based decision making algorithm....
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...Krishna et al. (2016) proposed a model for task offloading using a learning automata based decision-making algorithm....
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TL;DR: This paper analyzes the energy consumption characteristics of different components in mobile RFID systems, based on which a framework to perform energy-aware offloading for such systems is proposed, and illustrates how it can help offload computational intensive tasks to edge servers to save energy consumption on mobile readers while satisfying the constraint on total execution time.
Abstract: Internet of Things (IoT) have been widely used in many fields including smart city, industry Internet and automatic driving. Because IoT end devices usually have only limited capability in computation and power supply, they are not suitable to execute energy-consuming computational tasks. In many cases, we need to offload computational tasks from IoT end devices to edge servers in order to save energy consumption on the end devices. This process is usually termed as computing offloading. In this paper, we study computing offloading in radio frequency identification (RFID) systems built with mobile readers. We analyze the energy consumption characteristics of different components in mobile RFID systems, based on which we propose a framework to perform energy-aware offloading for such systems. By using tag searching as an example, we illustrate how our framework can help offload computational intensive tasks to edge servers to save energy consumption on mobile readers while satisfying the constraint on total execution time. Simulation results shown that the energy consumption of mobile readers can be greatly reduced by using our offloading framework.
42 citations
TL;DR: A novel energy sensitive task offloading strategy to offload tasks to cloud center, as well as other robots to greatly improve the computing ability and execution efficiency and prolong the lifetime of the robot network.
Abstract: Cloud robotic network (CRN) normally contains multiple mobile robots and a cloud computing center providing feasible solutions for many multiagent applications. One of the most critical issues in CRN and its application is how to effectively assign/offload computational tasks. This paper presents a novel energy sensitive task offloading strategy to answer the question particularly for CRN. First, we propose a novel strategy to offload tasks to cloud center, as well as other robots to greatly improve the computing ability and execution efficiency. Second, an energy sensitive model is developed to balance the energy level of the robots and eventually prolong the lifetime of the robot network. A modified genetic algorithm (GA), named energy sensitive GA, is finally developed and integrated into the strategy to get the optimized task offloading result as soon as possible, which is critical to most CRN applications. The correctness, efficiency, and scalability of the proposed strategy are proved with both theoretical analysis and experimental simulations. The evaluation results show that the proposed method can effectively assign tasks and prolong the lifetime of the network to a certain extent.
23 citations
Cites methods from "Learning automata based decision ma..."
...The computation task is offloaded to appropriate virtual machine on the cloud with consideration of the time required and the energy loss [22]....
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TL;DR: A comprehensive survey and taxonomy of the offloading approaches designed and proposed for MCCs is put forward, based on the algorithms which have been used for making the offload decisions, and illuminates how the off loading decisions are made to improve application performance and mobile devices' energy efficiency.
Abstract: The rapid developments in the mobile application context illuminate the demand for more resources and processing power at Smart Mobile Devices (SMDs). Mobile Cloud Computing (MCC) enables the SMDs to offload their workloads on the remote cloud servers and benefit from the MCC’s extensive resources to deal with this issue. To this end, numerous offloading schemes are provided in the literature to enhance the SMD's efficiency by offloading their workloads on the nearby cloudlets or remote cloud computing resources. This article puts forward a comprehensive survey and taxonomy of the offloading approaches designed and proposed for MCCs. It first classifies them based on the algorithms which have been used for making the offloading decisions. Then, in each category, it illuminates how the offloading decisions are made to improve application performance and mobile devices' energy efficiency, regarding offloading factors such as deadlines, costs, etc. The evaluation metrics, simulator, offloading type, and architecture of the studied schemes are compared and illuminated in each category. Furthermore, regarding the various properties of the studied offloading methods, the offloading domain's leading issues and challenges are discussed. Lastly, the concluding points are provided, and directions for the subsequent studies in the offloading context are specified.
21 citations
TL;DR: This paper uses the model of fungal growth as it happens in nature to adjust the attributes of the cells of the cellular learning automaton in order to take into consideration the dynamicity that exists in peer-to-peer networks in the process of super-peers selection.
Abstract: Super-peer networks refer to a class of peer-to-peer networks in which some peers called super-peers are in charge of managing the network. A group of super-peer selection algorithms use the capacity of the peers for the purpose of super-peer selection where the capacity of a peer is defined as a general concept that can be calculated by some properties, such as bandwidth and computational capabilities of that peer. One of the drawbacks of these algorithms is that they do not take into consideration the dynamic nature of peer-to-peer networks in the process of selecting super-peers. In this paper, an adaptive super-peer selection algorithm considering peers capacity based on an asynchronous dynamic cellular learning automaton has been proposed. The proposed cellular learning automaton uses the model of fungal growth as it happens in nature to adjust the attributes of the cells of the cellular learning automaton in order to take into consideration the dynamicity that exists in peer-to-peer networks in the process of super-peers selection. Several computer simulations have been conducted to compare the performance of the proposed super-peer selection algorithm with the performance of existing algorithms with respect to the number of super-peers, and capacity utilization. Simulation results have shown the superiority of the proposed super-peer selection algorithm over the existing algorithms.
20 citations
Cites background from "Learning automata based decision ma..."
...Learning automata have found applications in many areas such as sensor networks [24, 30], stochastic graphs [60], peer-to-peer networks [15, 61–64], channel assignment [65], mobile cloud computing [66] to mention a few....
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14,562 citations
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3,599 citations
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02 May 2005
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3,186 citations
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...The task T executes in the mobile environment....
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15 Jun 2010
TL;DR: MAUI supports fine-grained code offload to maximize energy savings with minimal burden on the programmer, and decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains.
Abstract: This paper presents MAUI, a system that enables fine-grained energy-aware offload of mobile code to the infrastructure. Previous approaches to these problems either relied heavily on programmer support to partition an application, or they were coarse-grained requiring full process (or full VM) migration. MAUI uses the benefits of a managed code environment to offer the best of both worlds: it supports fine-grained code offload to maximize energy savings with minimal burden on the programmer. MAUI decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains. In our evaluation, we show that MAUI enables: 1) a resource-intensive face recognition application that consumes an order of magnitude less energy, 2) a latency-sensitive arcade game application that doubles its refresh rate, and 3) a voice-based language translation application that bypasses the limitations of the smartphone environment by executing unsupported components remotely.
2,530 citations
10 Apr 2011
TL;DR: The design and implementation of CloneCloud is presented, a system that automatically transforms mobile applications to benefit from the cloud that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud.
Abstract: Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity with more powerful machines ranging from laptops and desktops to commercial clouds. This paper presents the design and implementation of CloneCloud, a system that automatically transforms mobile applications to benefit from the cloud. The system is a flexible application partitioner and execution runtime that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud. CloneCloud uses a combination of static analysis and dynamic profiling to partition applications automatically at a fine granularity while optimizing execution time and energy use for a target computation and communication environment. At runtime, the application partitioning is effected by migrating a thread from the mobile device at a chosen point to the clone in the cloud, executing there for the remainder of the partition, and re-integrating the migrated thread back to the mobile device. Our evaluation shows that CloneCloud can adapt application partitioning to different environments, and can help some applications achieve as much as a 20x execution speed-up and a 20-fold decrease of energy spent on the mobile device.
2,054 citations
"Learning automata based decision ma..." refers methods in this paper
...The energy consumption of the mobile Environment is given as MobileMobileMobile CTPE (3) where E is the energy consumption of the mobile environment, P is the power consumption, and CT is the completion time....
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