Elastic High Performance Applications -- A Composition Framework

Over the Virtual Top. Digital Service Value Chain Disintermediation Implications for Hybrid Heterogeneous Network Regulation

Today's complex cloud applications are composed of multiple components executed in multi-cloud environments. For such applications, the possibility to manage and control their cost, quality, and resource elasticity is of paramount importance. However, given that the cost of different services offered by cloud providers can vary a lot with their quality/performance, elasticity controllers must consider not only complex, multi-dimensional preferences and provisioning capabilities from stakeholders but also various runtime information regarding cloud applications and their execution environments. In this chapter, the authors present the elasticity control approach of the EU CELAR Project, which deals with multi-dimensional elasticity requirements and ensures multi-level elasticity control for fulfilling user requirements. They show the elasticity control mechanisms of the CELAR project, from application description to multi-level elasticity control. The authors highlight the usefulness of CELAR's mechanisms for users, who can use an intuitive, user-friendly interface to describe and then to follow their application elasticity behavior controlled by CELAR.

The recent increase in the use of cloud service has led to the need for cloud service broker(CSB) for supporting the use of various cloud computing platforms. CSB plays the role of not only providing overall services needed for individuals and companies to use cloud computing but also managing and inspecting them. As cloud computing became commonplace, it is expected that the demand for more useful cloud-based service at more affordable prices will increase exponentially. Accordingly, the current situation calls for CSB that connects cloud service provider & customer and provides the service of managing and inspecting cloud service. This paper presents a framework of the components and role of cloud service broker system that supports the allocation, control and management of virtual system based on brokering function between cloud service provider(CSP) and cloud service customer (CSC) by integrating and managing cloud resources in multiple heterogeneous cloud environment. Based on this, it is expected that this paper will contribute to the revitalization of cloud service industry.

With the arrival of auto-stereoscopic 3D displays for mobile devices, and emergence of more 3D content, there is much anticipation for 3D mobile multimedia experiences, including 3D display gaming. Simultaneously, with the emergence of cloud computing, more mobile applications are being developed to take advantage of the elastic cloud resources. In this paper, we explore the possibility of developing Cloud-based 3D Mobile Gaming, where the 3D video rendering and encoding is performed on cloud servers, with the resulting 3D video streamed over wireless networks to mobile devices with 3D displays for a true 3D mobile gaming experience. However, with the significantly higher bit rate requirement for 3D video, ensuring user experience may be a challenge, both in terms of 3D video quality and network delay (response time), considering the bandwidth constraints and fluctuations of wireless networks. In this paper, we propose a new asymmetric graphics rendering approach which can significantly reduce the video encoding bit rate needed for a certain video quality, thereby making it easier to transmit the video over wireless network. However, since asymmetric rendering may also impair the graphics quality, we need to be able to understand and measure its impact. We conduct subjective tests to study and model the impairments due to asymmetric rendering and network delay, thereby developing a user experience model for cloud based mobile 3D display gaming. By conducting subsequent subjective tests, we prove the correctness of the impairment functions and the resulting user experience model. We also conduct experiments using real 4G-LTE network profile. Experimental results show that by making use of the user experience model, it is possible to set appropriate graphics rendering parameters according to network constraints, such that the user experience can be maintained to a high level.

With the development of cloud computing technology, cloud services put forward higher requirements for Internet bandwidth, traffic, execution time, cost, etc. At the same time, more and more fierce market competition has led to uncontrollable online business, which has resulted in more and more network fluctuations and business fluctuations. These fluctuation risks make cloud services unstable and beyond expectations. Traditional cloud service scheduling algorithms are more based on time, resource balance, execution cost, etc., which are more suitable for stable environment. When there are fluctuations, these algorithms can not take the impact of fluctuations into account, resulting in unreasonable scheduling strategy. In this study, we propose a multi-objective optimization scheduling strategy for cloud service based on fluctuation cost. We use the fluctuation cost to evaluate the potential impact of the fluctuation, construct the resource sequence based on the fluctuation factor, and obtain the fluctuation cost value through the fluctuation cost algorithm. Combined with execution time optimization, task priority and other objectives, particle swarm optimization scheduling algorithm is improved to meet the multi-objective strategy requirements. Finally, the experiment proves that this strategy can better handle the resource scheduling problem when fluctuations occur. By setting different weight values, it can provide more solutions for user decision-making in the actual situation.

Composition of Cloud services is necessary when a single component is unable to satisfy all the user's requirements. It is a complex task for Cloud managers which involves several operations such as discovery, compatibility checking, selection, and deployment. Similarly to a non Cloud environment, the service composition raises the need for design-time approaches to check the correct interaction between the different components of a composite service. However, for Cloud-based service composition, new specific constraints, such as resources management, elasticity and multitenancy have to be considered. In this work, we use Symbolic Observation Graphs (SOG) in order to abstract Cloud services and to check the correction of their composition with respect to event- and state-based LTL formulae. The violation of such formulae can come either from the stakeholders' interaction or from the shared Cloud resources perspectives. In the former case, the involved services are considered as incompatible while, in the latter case, the problem can be solved by deploying additional resources. The approach we propose in this paper allows then to check whether the resource provider service is able, at run time, to satisfy the users' requests in terms of Cloud resources.

Cloud computing is currently the most hyped and popular paradigm in the domain of distributed computing. Multiple vendors are providing infrastructure, platform, and software-related cloud products or solutions, and the number of such vendors is growing rapidly. But the reality is that there is very little in the form of standards in general and interoperability standards in particular. This leads to a challenging situation for cloud service consumers as they need to use solutions provided by multiple cloud vendors in order to fulfill their business requirements, and the lack of standardization leads to lack of interoperability among the solutions provided by varied vendors. Service brokers act as intermediaries between the cloud service providers and consumers. The role of service brokers becomes critical in situations that need integration among multiple systems as they bridge the interoperability gaps among multiple cloud solutions and thus compensate for the lack of standards. They do so by acting as agents adding value on behalf of the service consumers. The usage of such cloud brokers also helps to future-proof the solution as service brokers add a layer of indirection, thus enabling loose coupling among the cloud service consumers and providers. The aim of this chapter is to review the role of cloud service brokers in cloud computing. The review includes consolidation of the definitions and characteristics of cloud service brokers, review of the current brokerage solutions, and interfacing requirements. Also presented are a high-level cloud service broker functional architecture, criteria for selection of a cloud service broker, future trends and review of benefits, and challenges of using cloud service brokers.

A novel energy-aware resource management technique using joint VM and container consolidation approach for green computing in cloud data centers

Resource elasticity and server consolidation have long been among two of cloud computing’s most relevant management tools. Yet, exemplified with a scientific application use case, this paper highlights how judicious scheduling of tasks can help maximize resource utilization and improve performance and costs for both users and cloud providers. Developing an efficient cloud service for DNA sequence comparisons is adopted as a motivating use case. Using the bioinformatics tool MASA that finds an optimal pair-wise sequence alignment, we propose a model for co-scheduling multiple alignments on a single cloud instance. The resulting, practically optimal, non-preemptive schedule can effectively double the throughput of MASA-based sequence alignment workflows.

Cloud based intelligent system for delivering health care as a service

BPPIR: Blockchain-assisted privacy-preserving similarity image retrieval over multiple clouds

A procedure is described that allows an operator to identify beam-pumped wells which are operating at reduced efficiency. A logical sequence of steps is presented for acquiring performance data such as power, dynamometer, and fluid level. The criteria to be used in determining the causes of inefficiency are presented. Following the logical methodology will reduce the time and effort required to perform the analysis. A field case is presented and discussed in detail. Introduction The need to increase oil production, reduce operating costs and increase net income from beam pumped wells, requires an integrated analysis of the pumping system including the performance and interaction of all the elements: the reservoir, the wellbore, the downhole pump, the rod string, the beam pump and the prime mover. Such system analysis can now be undertaken efficiently using portable lap-top-based data acquisition systems in conjunction with appropriate transducers and analysis software(1). Field experience undertaking such analysis in numerous wells has resulted in the development of a procedure which insures that good results are obtained with the minimum of effort. It is the objective of this paper to outline this procedure or methodology with the hope that it will be of use to production personnel throughout the industry. The end result of such system analysis should be the complete visualization of the performance of a given pumping well at a given time and a set of recommendations to realize improvement. In general the following steps should be undertaken:Establish the well's inflow performance to determine if additional production potential is available.Determine the overall energy efficiency as a means to identify wells which are candidates for detailed analysis.Analyse performance of downhole pump and downhole gas separator.Analyse mechanical loading of rods and beam pump.Analyse performance and loading of the prime mover.Design potential modifications to improve existing systemImplement changes and verify improvement. Although such a procedure is greatly facilitated through the use of an integrated data acquisition and analysis instrument(1) which includes all the necessary hardware and software components, it can be undertaken with a number of other devices and components currently commercially available from other manufacturers. Methodology The steps to be followed in defining the performance of a system should result in the maximum of information with a minimum of time and effort. This is facilitated by having access to accurate and timely information about the well's characteristics, completion and well tests. Although this may be initially time consuming it is generally a task that has to be completed only once. Some sort of data base management system is helpful in maintaining this information current. The information should be organized by well into a summary Well Data File which can be accessed by most of the application programs that are likely to be used. This eliminates having to re-enter the data. Figure 1 shows an example of such "Well Data File."

PurposeCloud computing becomes mobile when a mobile device tries to access the shared pool of computing resources provided by the cloud, on demand. Mobile applications may enrich their functionality by delegating heavy tasks to the clouds as the remote processing and storage have become possible by adding asynchronous behavior in the communication. However, developing mobile cloud applications involves working with services and APIs from different cloud vendors, which mostly are not interoperable across clouds. Moreover, by adding asynchronicity, mobile applications must rely on push mechanisms which are considered to be moderately reliable, and thus not recommended in scenarios that require high scalability and quality of service (QoS). To counter these problems, and the purpose of this paper, is to design a middleware framework, Mobile Cloud Middleware (MCM), which handles the interoperability issues and eases the use of process‐intensive services from smartphones by extending the concept of mobile host.Design/methodology/approachMCM is developed as an intermediary between the mobile and the cloud, which hides the complexity of dealing with multiple cloud services from mobiles. Several applications are presented to show the benefits of mobiles going cloud‐aware. Moreover, to verify the scalability of MCM, load tests are performed on the hybrid cloud resources using well known load balancing mechanisms like HAProxy and Tsung.FindingsFrom the study it was found that it is possible to handle hybrid cloud services from mobiles by using MCM. The analysis demonstrated that the MCM shows reasonable performance levels of interaction with the user, thus validating the proof of concept. Moreover, MCM decreases the effort in developing mobile cloud applications and helps in keeping soft‐real time responses by using its asynchronous approach.Originality/valueMCM fosters the utilization of different types of cloud services rather than the traditional mobile cloud services based on data synchronization. By offloading heavy tasks to the clouds, the framework extends the processing power and storage space capabilities of the constrained smart phones. The applications mentioned in the paper bring an added value by being success stories for mobile cloud computing domain in general.

A computing system for autonomous vehicles must efficiently process vast amounts of data from various sensors in real-time and have a backup design to handle system failures. Adding more computing devices improves performance and redundancy, but increases costs and energy consumption. With the development of vehicle-to-everything (V2X) communication technologies and edge computing, such as multiaccess edge computing (MEC), computation offloading has been introduced. This process involves the use of cloud or edge servers to handle calculations normally performed by on-board computers, thereby reducing their workload and offering redundancy. This article proposes an offloading strategy that optimizes the allocation of software components (SWCs) in autonomous driving software. By optimizing SWC allocation, SWCs can be effectively assigned to specific computing units. This article established a cost function and constraints based on SWC-specific features to address the offloading decision problem as an allocation optimization issue. This article also defines safety-related metrics, including the response time requirements and failure risks of SWCs, to set criteria for offloading decisions. The proposed SWC optimization method is tested and validated using a real autonomous driving vehicle demonstration involving both cloud and vehicular edge environments.

The development of mobile edge computing (MEC) is accelerating the popularity of 5G applications. In the 5G era, aiming to reduce energy consumption and latency, most applications or services are conducted on both edge cloud servers and cloud servers. However, the existing multi-cloud composition recommendation approaches are studied in the context of resources provided by a single cloud or multiple clouds. Hence, these approaches cannot cope with services requested by the composition of multiple clouds and edge clouds jointly in MEC. To this end, this paper firstly expands the structure of the multi-cloud service system and further constructs a multi-cloud multi-edge cloud (MCMEC) environment. Technically, we model this problem with formal concept analysis (FCA) by building the service–provider lattice and provider–cloud lattice, and select the candidate cloud composition that satisfies the user’s requirements. In order to obtain an optimized cloud combination that can efficiently reduce the energy consumption, money cost, and network latency, the skyline query mechanism is utilized for extracting the optimized cloud composition. We evaluate our approach by comparing the proposed algorithm to the random-based service composition approach. A case study is also conducted for demonstrating the effectiveness and superiority of our proposed approach.