The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.
Mobile cloud computing is an emerging research area. Honeybee @cite_4 is a framework to enable mobile devices to offload tasks, utilize resources from devices, and perform human-aided computations. Huerta-Canepa @cite_5 proposed an architecture to offload computation to nearby devices using P2P techniques. Flores and Srirama @cite_50 proposed an approach for mobile cloud by exploring a middleware component between the devices and the cloud. @cite_48 proposed an approach to offload parts of a computation to the cloud to reduce energy consumption on the device.
@cite_5 @cite_4 @cite_50 @cite_48
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Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases. Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources—the transmit precoding matrices of the MUs—and the computational resources—the CPU cycles second assigned by the cloud to each MU—in order to minimize the overall users’ energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms. Mobile cloud computing is a promising technique that shifts the data and computing service modules from individual devices to a geographically distributed cloud service architecture. A general mobile cloud computing system is comprised of multiple cloud domains, and each domain manages a portion of the cloud system resources, such as the Central Processing Unit, memory and storage, etc. How to efficiently manage the cloud resources across multiple cloud domains is critical for providing continuous mobile cloud services. In this paper, we propose a service decision making system for interdomain service transfer to balance the computation loads among multiple cloud domains. Our system focuses on maximizing the rewards for both the cloud system and the users by minimizing the number of service rejections that degrade the user satisfaction level significantly. To this end, we formulate the service request decision making process as a semi-Markov decision process. The optimal service transfer decisions are obtained by jointly considering the system incomes and expenses. Extensive simulation results show that the proposed decision making system can significantly improve the system rewards and decrease service disruptions compared with the greedy approach. The contribution of cloud computing and mobile computing technologies lead to the newly emerging mobile cloud computing paradigm. Three major approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study how to optimize the computation partitioning of a data stream application between mobile and cloud to achieve maximum speed throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the makespan of executions as in other applications. We first propose a framework to provide runtime support for the dynamic computation partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm for optimal computation partition. Both numerical evaluation and real world experiment have been performed, and the results show that the partitioned application can achieve at least two times better performance in terms of throughput than the application without partitioning.