An energy-aware QoS enhanced method for service computing across clouds and data centers

QoS plays a key role in evaluating a service or a service composition plan across clouds and data centers. Currently, the energy cost of a service's execution is not covered by the QoS framework, and a service's price is often fixed during its execution. However, energy consumption has a great contribution in determining the price of a cloud service. As a result, it is not reasonable if the price of a cloud service is calculated with a fixed energy consumption value, if part of a service's energy consumption could be saved during its execution. Taking advantage of the dynamic energy-Aware optimal technique, a QoS enhanced method for service computing is proposed, in this paper, through virtual machine (VM) scheduling. Technically, two typical QoS metrics, i.e., the price and the execution time are taken into consideration in our method. Moreover, our method consists of two dynamic optimal phases. The first optimal phase aims at dynamically benefiting a user with discount price by transparently migrating his or her task execution from a VM located at a server with high energy consumption to a low one. The second optimal phase aims at shortening task's execution time, through transparently migrating a task execution from a VM to another one located at a server with higher performance. Experimental evaluation upon large scale service computing across clouds demonstrates the validity of our method.

Service-oriented wireless sensor networks and an energy-aware mesh routing algorithm

Service-oriented wireless sensor networks (WSNs) are being paid more and more attention because service computing can hide complexity of WSNs and enables simple and transparent access to individual sensor nodes. Existing WSNs mainly use IEEE 802.15.4 as their communication specification, however, this protocol suite cannot support IP-based routing and service-oriented access because it only specifies a set of physical- and MAC-layer protocols. For inosculating WSNs with IP networks, IEEE proposed a 6LoWPAN (IPv6 over LoW Power wireless Area Networks) as the adaptation layer between IP and MAC layers. However, it is still a challenging task how to discover and manage sensor resources, guarantee the security of WSNs and route messages over resource-restricted sensor nodes. This paper is set to address such three key issues. Firstly, we propose a service-oriented WSN architectural model based on 6LoWPAN and design a lightweight service middleware SOWAM (service-oriented WSN architecture middleware), where each sensor node provides a collection of services and is managed by our SOWAM. Secondly, we develop a security mechanism for the authentication and secure connection among users and sensor nodes. Finally, we propose an energyaware mesh routing protocol (EAMR) for message transmission in a WSN with multiple mobile sinks, aiming at prolonging the lifetime of WSNs as long as possible. In our EAMR, sensor nodes with the residual energy lower than a threshold do not forward messages for other nodes until the threshold is leveled down. As a result, the energy consumption is evened over sensor nodes significantly. The experimental results demonstrate the feasibility of our service-oriented approach and lightweight middleware SOWAM, as well as the effectiveness of our routing algorithm EAMR.

An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method.

Performance optimization for energy-aware adaptive checkpointing in embedded real-time systems

Using additional store-checkpoinsts (SCPs) and compare-checkpoints (CCPs), we present an adaptive checkpointing for double modular redundancy (DMR) in this paper. The proposed approach can dynamically adjust the checkpoint intervals. We also design methods to calculate the optimal numbers of checkpoints, which can minimize the average execution time of tasks. Further, the adaptive checkpointing is combined with the DVS (dynamic voltage scaling) scheme to achieve energy reduction. Simulation results show that, compared with the previous methods, the proposed approach significantly increases the likelihood of timely task completion and reduces energy consumption in the presence of faults.

Special issue : Big data and cloud computing challenges

This special issue aims to discuss the issues and challenges in maintaining big data, which is now becoming a major issue for our technical environments. It will address the emerging problems of the 5 Vs of the data landscape: volume, variety, velocity, veracity and value.

A block-aware hybrid data dissemination with hotspot elimination in wireless sensor network

As a significant milestone in the data dissemination of wireless sensor networks (WSNs), the comb-needle (CN) model was developed to dynamically balance the sensor data pushing and pulling during hybrid data dissemination. Unfortunately, the hybrid push-pull data dissemination strategy may overload some sensor nodes and form the hotspots that consume energy significantly. This usually leads to the collapse of the network at a very early stage. In the past decade, although many energy-aware dynamic data dissemination methods have been proposed to alleviate the hotspots issue, the block characteristic of sensor nodes has been overlooked and how to offload traffic from hot blocks with low energy through long-distance hybrid dissemination remains an open problem. In this paper, we developed a block-aware data dissemination model to balance the inter-block energy and eliminate the spreading of intra-block hotspots. Through the clustering mechanism based on geography and energy, "similar" large-scale sensor nodes can be efficiently grouped into specific blocks to form the global block information (GBI). Based on GBI, the long-distance block-cross hybrid algorithms are further developed by effectively aggregating inter-block and intra-block data disseminations. Extensive experimental results demonstrate the capability and the efficiency of the proposed approach. © 2014 Elsevier Ltd.

A shadow-like task migration model based on context semantics for mobile and pervasive environments

Pervasive computing is a user-centric mobile computing paradigm, in which tasks should be migrated over different platforms in a shadow-like way when users move around. In this paper, we propose a context-sensitive task migration model that recovers program states and rebinds resources for task migrations based on context semantics through inserting resource description and state description sections in source programs. Based on our model, we design and develop a task migration framework xMozart which extends the Mozart platform in terms of context awareness. Our approach can recover task states and rebind resources in the context-aware way, as well as support multi- modality I/O interactions. The extensive experiments demonstrate that our approach can migrate tasks by resuming them from the last broken points like shadows moving along with the users.

Adaptive energy-efficient qos-aware scheduling algorithm for TCP/IP mobile cloud

The expected pervasive use of mobile cloud computing and the growing number of Internet data centers have brought forth many concerns, such as, energy costs and energy saving management of both data centers and mobile connections. Therefore, the need for adaptive and distributed resource allocation schedulers for minimizing the communication-plus-computing energy consumption has become increasingly important. In this paper, we propose and test an efficient dynamic resource provisioning scheduler that jointly minimizes computation and communication energy consumption, while guaranteeing user Quality of Service (QoS) constraints. We evaluate the performance of the proposed dynamic resource provisioning algorithm with respect to the execution time, goodput and bandwidth usage and compare the performance of the proposed scheduler against the exiting approaches. The attained experimental results show that the proposed dynamic resource provisioning algorithm achieves much higher energy-saving than the traditional schemes.

Energy-efficient management of data center resources for cloud computing : a vision, architectural elements, and open challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.

Affect computing in film through sound energy dynamics

We develop an algorithm for the detection and classification of affective sound events underscored by specific patterns of sound energy dynamics. We relate the portrayal of these events to proposed high level affect or emotional coloring of the events. In this paper, four possible characteristic sound energy events are identified that convey well established meanings through their dynamics to portray and deliver certain affect, sentiment related to the horror film genre. Our algorithm is developed with the ultimate aim of automatically structuring sections of films that contain distinct shades of emotion related to horror themes for nonlinear media access and navigation. An average of 82% of the energy events, obtained from the analysis of the audio tracks of sections of four sample films corresponded correctly to the proposed affect. While the discrimination between certain sound energy event types was low, the algorithm correctly detected 71% of the occurrences of the sound energy events within audio tracks of the films analyzed, and thus forms a useful basis for determining affective scenes characteristic of horror in movies.