Abstract-Smartphones are now capable of supporting a wide range of applications, many of which demand an ever increasing computational power. This poses a challenge because smartphones are resource-constrained devices with limited computation power, memory, storage, and energy. Fortunately, the cloud computing technology offers virtually unlimited dynamic resources for computation, storage, and service provision. Therefore, researchers envision extending cloud computing services to mobile devices to overcome the smartphones constraints. The challenge in doing so is that the traditional smartphone application models do not support the development of applications that can incorporate cloud computing features and requires specialized mobile cloud application models. This article presents mobile cloud architecture, offloading decision affecting entities, application models classification, the latest mobile cloud application models, their critical analysis and future research directions.

No abstract

No abstract

Two major constraints demand more consideration for energy efficiency in cluster computing: (a) operational costs, and (b) system reliability. Increasing energy efficiency in cluster systems will reduce energy consumption, excess heat, lower operational costs, and improve system reliability. Based on the energy-power relationship, and the fact that energy consumption can be reduced examples. The survey is concluded with a brief discussion and some assumptions about the possible future directions that could be explored to improve the energy efficiency in cluster computing.

Cloud computing has emerged as the leading paradigm for information technology businesses. Cloud computing provides a platform to manage and deliver computing services around the world over the Internet. Cloud services have helped businesses utilize computing services on demand with no upfront investments. The cloud computing paradigm has sustained its growth, which has led to increase in size and number of data centers. Data centers with thousands of computing devices are deployed as back end to provide cloud services. Computing devices are deployed redundantly in data centers to ensure 24/7 availability. However, many studies have pointed out that data centers consume large amount of electricity, thus calling for energy-efficiency measures. In this survey, we discuss research issues related to conflicting requirements of maximizing quality of services (QoSs) (availability, reliability, etc.) delivered by the cloud services while minimizing energy consumption of the data center resources. In this paper, we present the concept of inception of data center energy-efficiency controller that can consolidate data center resources with minimal effect on QoS requirements. We discuss software-and hardware-based techniques and architectures for data center resources such as server, memory, and network devices that can be manipulated by the data center controller to achieve energy efficiency.Index Terms-Controller design, data centers, energy efficiency.

This paper presents a novel position-based routing protocol for vehicular ad hoc networks (VANETs) to enhance traffic safety and traffic organization and facilitate driving through a smart transportation system. The protocol is referred to as the traffic flow-oriented routing (TFOR) protocol for VANETs. It considers a real-time urban scenario with multi-lane and bi-directional roads. It chooses junction optimally considering vehicular traffic flows to accomplish robust routing paths and thereby forwarding the data packets. The new junction selection mechanism and routing between the junctions is based on two-hop neighbor information, which increases packet-delivery ratio and decreases end-to-end delay. We designed, implemented, and compared TFOR against existing routing protocols of VANETs (greedy-perimeter stateless routing (GPSR), geographic source routing (GSR), and enhanced greedy traffic-aware routing (E-GyTAR)). Simulation outcomes in urban scenarios show that TFOR minimizes average end-to-end delay and routing overhead by on average 15.3% and 19.5%, respectively, compared to GPSR. It reduces routing overhead up to 17% compared to GSR. TFOR maximizes packet-delivery ratio on an average of 17.5%, 10.7%, and 7.2% compared to GPSR, GSR, and E-GyTAR, respectively.

No abstract

No abstract