SAMI: Service-Based Arbitrated Multi-Tier Infrastructure for Mobile Cloud Computing

Mobile Cloud Computing (MCC) is the state-ofthe- art mobile computing technology aims to alleviate resource poverty of mobile devices. Recently, several approaches and techniques have been proposed to augment mobile devices by leveraging cloud computing. However, long-WAN latency and trust are still two major issues in MCC that hinder its vision. In this paper, we analyze MCC and discuss its issues. We leverage Service Oriented Architecture (SOA) to propose an arbitrated multi-tier infrastructure model named SAMI for MCC. Our architecture consists of three major layers, namely SOA, arbitrator, and infrastructure. The main strength of this architecture is in its multi-tier infrastructure layer which leverages infrastructures from three main sources of Clouds, Mobile Network Operators (MNOs), and MNOs' authorized dealers. On top of the infrastructure layer, an arbitrator layer is designed to classify Services and allocate them the suitable resources based on several metrics such as resource requirement, latency and security. Utilizing SAMI facilitate development and deployment of service-based platform-neutral mobile applications.Comment: 6 full pages, accepted for publication in IEEE MobiCC'12 conference, MobiCC 2012:IEEE Workshop on Mobile Cloud Computing, Beijing, Chin

Mobile Cloud Computing: A Review on Smartphone Augmentation Approaches

Smartphones have recently gained significant popularity in heavy mobile processing while users are increasing their expectations toward rich computing experience. However, resource limitations and current mobile computing advancements hinder this vision. Therefore, resource-intensive application execution remains a challenging task in mobile computing that necessitates device augmentation. In this article, smartphone augmentation approaches are reviewed and classified in two main groups, namely hardware and software. Generating high-end hardware is a subset of hardware augmentation approaches, whereas conserving local resource and reducing resource requirements approaches are grouped under software augmentation methods. Our study advocates that consreving smartphones' native resources, which is mainly done via task offloading, is more appropriate for already-developed applications than new ones, due to costly re-development process. Cloud computing has recently obtained momentous ground as one of the major cornerstone technologies in augmenting smartphones. We present sample execution model for intensive mobile applications and devised taxonomy of augmentation approaches. For better comprehension, the results of this study are summarized in a table

Tripod of Requirements in Horizontal Heterogeneous Mobile Cloud Computing

Recent trend of mobile computing is emerging toward executing resource-intensive applications in mobile devices regardless of underlying resource restrictions (e.g. limited processor and energy) that necessitate imminent technologies. Prosperity of cloud computing in stationary computers breeds Mobile Cloud Computing (MCC) technology that aims to augment computing and storage capabilities of mobile devices besides conserving energy. However, MCC is more heterogeneous and unreliable (due to wireless connectivity) compare to cloud computing. Problems like variations in OS, data fragmentation, and security and privacy discourage and decelerate implementation and pervasiveness of MCC. In this paper, we describe MCC as a horizontal heterogeneous ecosystem and identify thirteen critical metrics and approaches that influence on mobile-cloud solutions and success of MCC. We divide them into three major classes, namely ubiquity, trust, and energy efficiency and devise a tripod of requirements in MCC. Our proposed tripod shows that success of MCC is achievable by reducing mobility challenges (e.g. seamless connectivity, fragmentation), increasing trust, and enhancing energy efficiency

FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method

Job scheduling is one of the most important research problems in distributed systems, particularly cloud environments/computing. The dynamic and heterogeneous nature of resources in such distributed systems makes optimum job scheduling a non-trivial task. Maximal resource utilization in cloud computing demands/necessitates an algorithm that allocates resources to jobs with optimal execution time and cost. The critical issue for job scheduling is assigning jobs to the most suitable resources, considering user preferences and requirements. In this paper, we present a hybrid approach called FUGE that is based on fuzzy theory and a genetic algorithm (GA) that aims to perform optimal load balancing considering execution time and cost. We modify the standard genetic algorithm (SGA) and use fuzzy theory to devise a fuzzy-based steady-state GA in order to improve SGA performance in term of makespan. In details, the FUGE algorithm assigns jobs to resources by considering virtual machine (VM) processing speed, VM memory, VM bandwidth, and the job lengths. We mathematically prove our optimization problem which is convex with well-known analytical conditions (specifically, Karush–Kuhn–Tucker conditions). We compare the performance of our approach to several other cloud scheduling models. The results of the experiments show the efficiency of the FUGE approach in terms of execution time, execution cost, and average degree of imbalance

Erratum to: FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method[Cluster Comput DOI: 10.1007/s10586-014-0420-x]

Job scheduling is one of the most important research problems in distributed systems, particularly cloud environments/computing. The dynamic and heterogeneous nature of resources in such distributed systems makes optimum job scheduling a non-trivial task. Maximal resource utilization in cloud computing demands/necessitates an algo-rithm that allocates resources to jobs with optimal execu-tion time and cost. The critical issue for job scheduling is assigning jobs to the most suitable resources, considering user preferences and requirements. In this paper, we present a hybrid approach called FUGE that is based on fuzzy theory and a genetic algorithm (GA) that aims to perform optimal load balancing considering execution time and cost. We modify the standard genetic algorithm (SGA) and use fuzzy theory to devise a fuzzy-based steady-state GA in order to improve SGA performance in term of makespan. In details, the FUGE algorithm assigns jobs to resources by considering virtual machine (VM) processing speed, VM memory, VM bandwidth, and the job lengths. We mathematically prove ouroptimization problem which is convex with well-known analytical conditions (specifically, Karush–Kuhn–Tucker conditions). We compare the performance of our approach to several other cloud scheduling models. The results of the experiments show the efficiency of the FUGE approach in terms of execution time, execution cost, and average degree of imbalance

An energy-aware scheduling algorithm in DVFS-Enabled Networked Data Centers

In this paper, we propose an adaptive online energy-aware scheduling algorithm by exploiting the reconfiguration capability of a Virtualized Networked Data Centers (VNetDCs) processing large amount of data in parallel. To achieve energy efficiency in such intensive computing scenarios, a joint balanced provisioning and scaling of the networking-plus-computing resources is required. We propose a scheduler that manages both the incoming workload and the VNetDC infrastructure to minimize the communication-plus-computing energy dissipated by processing incoming traffic under hard real-time constraints on the per-job computing-plus-communication delays. Specifically, our scheduler can distribute the workload among multiple virtual machines (VMs) and can tune the processor frequencies and the network bandwidth. The energy model used in our scheduler is rather sophisticated and takes into account also the internal/external frequency switching energy costs. Our experiments demonstrate that the proposed scheduler guarantees high quality of service to the users respecting the service level agreements. Furthermore, it attains minimum energy consumptions under two real-world operating conditions: a discrete and finite number of CPU frequencies and not negligible VMs reconfiguration costs. Our results confirm that the overall energy savings of data center can be significantly higher with respect to the existing solutions

MOMCC: market-oriented architecture for mobile cloud computing based on service oriented architecture

The vision of augmenting computing capabilities of mobile devices, especially smartphones with least cost is likely transforming to reality leveraging cloud computing. Cloud exploitation by mobile devices breeds a new research domain called Mobile Cloud Computing (MCC). However, issues like portability and interoperability should be addressed for mobile augmentation which is a non-trivial task using component-based approaches. Service Oriented Architecture (SOA) is a promising design philosophy embraced by mobile computing and cloud computing communities to stimulate portable, complex application using prefabricated building blocks called Services. Utilizing distant cloud resources to host and run Services is hampered by long WAN latency. Exploiting mobile devices in vicinity alleviates long WAN latency, while creates new set of issues like Service publishing and discovery as well as client-server security, reliability, and Service availability. In this paper, we propose a market-oriented architecture based on SOA to stimulate publishing, discovering, and hosting Services on nearby mobiles, which reduces long WAN latency and creates a business opportunity that encourages mobile owners to embrace Service hosting. Group of mobile phones simulate a nearby cloud computing platform. We create new role of \textit{Service host} by enabling unskilled mobile owners/users to host Services developed by skilled developers. Evidently, Service availability, reliability, and Service-oriented mobile application portability will increase towards green ubiquitous computing in our mobile cloud infrastructure