Data stream management systems (DSMSs) are scalable, highly available, and fault-tolerant systems that aggregate and analyze real-time data in motion. To continuously perform analytics on the fly within the stream, state-of-the-art DSMSs host streaming applications as a set of interconnected operators, with each operator encapsulating the semantic of a specific operation. For parallel execution on a particular platform, these operators need to be appropriately replicated in multiple instances that split and process the workload simultaneously. Because the way operators are partitioned affects the resulting performance of streaming applications, it is essential for DSMSs to have a method to compare different operators and make holistic replication decisions to avoid performance bottlenecks and resource wastage. To this end, we propose a stepwise profiling approach to optimize application performance on a given execution platform. It automatically scales distributed computations over streams based on application features and processing power of provisioned resources and builds the relationship between provisioned resources and application performance metrics to evaluate the efficiency of the resulting configuration. Experimental results confirm that the proposed approach successfully fulfills its goals with minimal profiling overhead.

Mobile cloud computing is emerging as a promising approach to enrich user experiences at the mobile device end. Computation offloading in a heterogeneous mobile cloud environment has recently drawn increasing attention in research. The computation offloading decision making and tasks scheduling among heterogeneous shared resources in mobile clouds are becoming challenging problems in terms of providing global optimal task response time and energy efficiency. In this article, we address these two problems together in a heterogeneous mobile cloud environment as an optimization problem. Different from conventional distributed computing system scheduling problems, our joint offloading and scheduling optimization problem considers unique contexts of mobile clouds such as wireless network connections and mobile device mobility, which makes the problem more complex. We propose a context-aware mixed integer programming model to provide off-line optimal solutions for making the offloading decisions and scheduling the offloaded tasks among the shared computing resources in heterogeneous mobile clouds. The objective is to minimize the global task completion time (i.e., makespan). To solve the problem in real time, we further propose a deterministic online algorithm—the Online Code Offloading and Scheduling (OCOS) algorithm—based on the rent/buy problem and prove the algorithm is 2-competitive. Performance evaluation results show that the OCOS algorithm can generate schedules that have around two times shorter makespan than conventional independent task scheduling algorithms. Also, it can save around 30% more on makespan of task execution schedules than conventional offloading strategies, and scales well as the number of users grows.

Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real‐time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim , to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.

Summary Containers are increasingly gaining popularity and becoming one of the major deployment models in cloud environments. To evaluate the performance of scheduling and allocation policies in containerized cloud data centers, there is a need for evaluation environments that support scalable and repeatable experiments. Simulation techniques provide repeatable and controllable environments, and hence, they serve as a powerful tool for such purpose. This paper introduces ContainerCloudSim , which provides support for modeling and simulation of containerized cloud computing environments. We developed a simulation architecture for containerized clouds and implemented it as an extension of CloudSim. We described a number of use cases to demonstrate how one can plug in and compare their container scheduling and provisioning policies in terms of energy efficiency and SLA compliance. Our system is highly scalable as it supports simulation of large number of containers, given that there are more containers than virtual machines in a data center. Copyright © 2016 John Wiley & Sons, Ltd.

Mobile cloud computing (MCC) has become a significant paradigm for bringing the benefits of cloud computing to mobile devices' proximity. Service availability along with performance enhancement and energy efficiency are primary targets in MCC. This paper proposes a code offloading framework, called mCloud, which consists of mobile devices, nearby cloudlets and public cloud services, to improve the performance and availability of the MCC services. The effect of the mobile device context (e.g., network conditions) on offloading decisions is studied by proposing a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and appropriate cloud resources for offloading. We also investigate failure detection and recovery policies for our mCloud system. We explain in details the design and implementation of the mCloud prototype framework. We conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm are able to provide decisions on selecting wireless medium and cloud resources based on different context of the mobile devices, and achieve significant reduction on makespan and energy, with the improved service availability when compared with existing offloading schemes.

Power management of cloud data centers has received great attention from industry and academia as they are expensive to operate due to their high energy consumption. While hosts are dominant to consume electric power, networks account for 10 to 20 percent of the total energy costs in a data center. Resource overbooking is one way to reduce the usage of active hosts and networks by placing more requests to the same amount of resources. Network resource overbooking can be facilitated by Software Defined Networking (SDN) that can consolidate traffics and control Quality of Service (QoS) dynamically. However, the existing approaches employ fixed overbooking ratio to decide the amount of resources to be allocated, which in reality may cause excessive Service Level Agreements (SLA) violation with workloads being unpredictable. In this paper, we propose dynamic overbooking strategy which jointly leverages virtualization capabilities and SDN for VM and traffic consolidation. With the dynamically changing workload, the proposed strategy allocates more precise amount of resources to VMs and traffics. This strategy can increase overbooking in a host and network while still providing enough resources to minimize SLA violations. Our approach calculates resource allocation ratio based on the historical monitoring data from the online analysis of the host and network utilization without any pre-knowledge of workloads. We implemented it in simulation environment in large scale to demonstrate the effectiveness in the context of Wikipedia workloads. Our approach saves energy consumption in the data center while reducing SLA violations.

Energy usage of large-scale data centers has become a major concern for cloud providers. There has been an active effort in techniques for the minimization of the energy consumed in the data centers. However, most approaches lack the analysis and application of real cloud backend traces. In existing approaches, the variation of cloud workloads and its effect on the performance of the solutions are not investigated. Furthermore, the focus of existing approaches is on virtual machine migration and placement algorithms, with little regard to tailoring virtualmachine configuration to workload characteristics, which can further reduce the energy consumption and resource wastage in a typical data center. To address these weaknesses and challenges, we propose a new architecture for cloud resource allocation that maps groups of tasks to customized virtual machine types. This mapping is based on the task usage patterns obtained from the analysis of the historical data extracted from utilization traces. In our work, the energy consumption is decreased via efficient resource allocation based on the actual resource usage of tasks. Experimental results show that, when resources are allocated based on the discovered usage patterns, significant energy saving can be achieved.

It is common for cloud data centers meeting unexpected loads like request bursts, which may lead to overloaded situation and performance degradation. Dynamic Voltage Frequency Scaling and VM consolidation have been proved effective to manage overloads. However, they cannot function when the whole data center is overloaded. Brownout provides a promising direction to avoid overloads through configuring applications to temporarily degrade user experience. Additionally, brownout can also be applied to reduce data center energy consumption. As a complementary option for Dynamic Voltage Frequency Scaling and VM consolidation, our combined brownout approach reduces energy consumption through selectively and dynamically deactivating application optional components, which can also be applied to self-contained microservices. The results show that our approach can save more than 20 percent energy consumption and there are trade-offs between energy saving and discount offered to users.

Mobile cloud computing (MCC) has drawn significant research attention as the popularity and capability of mobile devices have been improved in recent years. In this paper, we propose a prototype MCC offloading system that considers multiple cloud resources such as mobile ad-hoc network, cloudlet and public clouds to provide an adaptive MCC service. We propose a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and which potential cloud resources as the offloading location based on the device context. We also conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm can select suitable wireless medium and cloud resources based on different context of the mobile devices, and achieve significant performance improvement.

Software-Defined Networking not only addresses the shortcoming of traditional network technologies in dealing with frequent and immediate changes in cloud data centers but also made network resource management open and innovation-friendly. To further accelerate the innovation pace, accessible and easy-to-learn testbeds are required which estimate and measure the performance of network and host capacity provisioning approaches simultaneously within a data center. This is a challenging task and is often costly if accomplished in a physical environment. Thus, a lightweight and scalable simulation environment is necessary to evaluate the network allocation capacity policies while avoiding such a complicated and expensive facility. This paper introduces CloudSimSDN, a simulation framework for SDN-enabled cloud environments based on CloudSim. This paper develops and presents the overall architecture and features of the framework and provides several use cases. Moreover, we empirically validate the accuracy and effectiveness of CloudSimSDN through a number of simulations of a cloud-based three-tier web application.

The emergence of cloud computing has made dynamic provisioning of elastic capacity to applications on-demand. Cloud data centers contain thousands of physical servers hosting orders of magnitude more virtual machines that can be allocated on demand to users in a pay-as-you-go model. However, not all systems are able to scale up by just adding more virtual machines. Therefore, it is essential, even for scalable systems, to project workloads in advance rather than using a purely reactive approach. Given the scale of modern cloud infrastructures generating real time monitoring information, along with all the information generated by operating systems and applications, this data poses the issues of volume, velocity, and variety that are addressed by Big Data approaches. In this paper, we investigate how utilization of Big Data analytics helps in enhancing the operation of cloud computing environments. We discuss diverse applications of Big Data analytics in clouds, open issues for enhancing cloud operations via Big Data analytics, and architecture for anomaly detection and prevention in clouds along with future research directions.

There has been a growing effort in decreasing energy consumption of large-scale cloud data centers via maximization of host-level utilization and load balancing techniques. However, with the recent introduction of Container as a Service (CaaS) by cloud providers, maximizing the utilization at virtual machine (VM) level becomes essential. To this end, this paper focuses on finding efficient virtual machine sizes for hosting containers in such a way that the workload is executed with minimum wastage of resources on VM level. Suitable VM sizes for containers are calculated, and application tasks are grouped and clustered based on their usage patterns obtained from historical data. Furthermore, tasks are mapped to containers and containers are hosted on their associated VM types. We analyzed clouds' trace logs from Google cluster and consider the cloud workload variances, which is crucial for testing and validating our proposed solutions. Experimental results showed up to 7.55% improvement in the average energy consumption compared to baseline scenarios where the virtual machine sizes are fixed. In addition, comparing to the baseline scenarios, the total number of VMs instantiated for hosting the containers is also improved by 68% on average.

Cloud service level agreement negotiation is a process of joint decision-making between cloud clients and providers to resolve their conflicting objectives. With the advances of cloud technology, operations such as discovery, scaling, monitoring and decommissioning are accomplished automatically. Therefore, negotiation between cloud clients and providers can be a bottleneck if it is carried out manually. Our objective is to propose a state-of-the-art solution to automate the negotiation process for cloud environments and specifically infrastructure as a service category. The proposed negotiation strategy is based on a time-dependent tactic. For cloud providers, the strategy uniquely considers utilization of resources when generating new offers and automatically adjusts the tactic's parameters to concede more on the price of less utilized resources. In addition, while the previous negotiation strategies in literature trust offered quality of service values regardless of their dependability, our proposed strategy is capable of assessing reliability of offers received from cloud providers. Furthermore, to find the right configuration of the time-dependent tactic in cloud computing environments, we investigate the effect of modifying parameters such as initial offer value and deadline on negotiation outputs that include ratio of deals made, and inequality index. The proposed negotiation strategy is tested with different workloads and in diverse market conditions to show how the time-dependent tactic's settings can dynamically adapt to help cloud providers increase their profits.

SUMMARY The cloud computing paradigm allows on‐demand access to computing and storage services over the Internet. Multiple providers are offering a variety of software solutions in the form of virtual appliances and computing units in the form of virtual machines with different pricing and QoS in the market. Thus, it is important to exploit the benefit of hosting virtual appliances on multiple providers to not only reduce the cost and provide better QoS but also achieve failure‐resistant deployment. This paper presents a framework called CloudPick to simplify cross‐cloud deployment and particularly focuses on QoS modeling and deployment optimization. For QoS modeling, cloud services have been automatically enriched with semantic descriptions using our translator component to increase precision and recall in discovery and benefit from descriptive QoS from multiple domains. In addition, an optimization approach for deploying networks of appliances is required to guarantee minimum cost, low latency, and high reliability. We propose and compare two different deployment optimization approaches: genetic‐based and forward‐checking‐based backtracking. They take into account QoS criteria such as reliability, data communication cost, and latency between multiple clouds to select the most appropriate combination of virtual machines and appliances. We evaluate our approach using a real case study and different request types. Experimental results suggest that both algorithms reach near‐optimal solution. Further, we investigate the effects of factors such as latency, reliability requirements, and data communication between appliances on the performance of the algorithms and placement of appliances across multiple clouds. The results show the efficiency of optimization algorithms depends on the data transfer rate between appliances. Copyright © 2014 John Wiley & Sons, Ltd.

When a single Cloud service (i.e., a software image and a virtual machine), on its own, cannot satisfy all the user requirements, a composition of Cloud services is required. Cloud service composition, which includes several tasks such as discovery, compatibility checking, selection, and deployment, is a complex process and users find it difficult to select the best one among the hundreds, if not thousands, of possible compositions available. Service composition in Cloud raises even new challenges caused by diversity of users with different expertise requiring their applications to be deployed across difference geographical locations with distinct legal constraints. The main difficulty lies in selecting a combination of virtual appliances (software images) and infrastructure services that are compatible and satisfy a user with vague preferences. Therefore, we present a framework and algorithms which simplify Cloud service composition for unskilled users. We develop an ontology-based approach to analyze Cloud service compatibility by applying reasoning on the expert knowledge. In addition, to minimize effort of users in expressing their preferences, we apply combination of evolutionary algorithms and fuzzy logic for composition optimization. This lets users express their needs in linguistics terms which brings a great comfort to them compared to systems that force users to assign exact weights for all preferences.

Cloud computing paradigm allows subscription-based access to computing and storages services over the Internet. Since with advances of Cloud technology, operations such as discovery, scaling, and monitoring are accomplished automatically, negotiation between Cloud service requesters and providers can be a bottleneck if it is carried out by humans. Therefore, our objective is to offer a state-of-the-art solution to automate the negotiation process in Cloud environments. In previous works in the SLA negotiation area, requesters trust whatever QoS criteria values providers offer in the process of negotiation. However, the proposed negotiation strategy for requesters in this work is capable of assessing reliability of offers received from Cloud providers. In addition, our proposed negotiation strategy for Cloud providers considers utilization of resources when it generates new offers during negotiation and concedes more on the price of less utilized resources. The experimental results show that our strategy helps Cloud providers to increase their profits when they are participating in parallel negotiation with multiple requesters.

SUMMARY Cloud computing offers virtualized computing elements on demand in a pay‐as‐you‐go manner. The major motivations to adopt Cloud services include no upfront investment on infrastructure and transferring responsibility of maintenance, backups, and license management to Cloud Providers. However, one of the key challenges that holds businesses from adopting Cloud computing services is that, by migrating to Cloud, they move some of their information and services out of their direct control. Their main concern is how well the Cloud providers keep their information (security) and deliver their services (performance). To cope with this challenge, several service level agreement management systems have been proposed. However, monitoring service deployment as a major responsibility of those systems have not been deeply investigated yet. Therefore, this paper shows how monitoring services have to be described, deployed (discovered and ranked), and then how they have to be executed to enforce accurate penalties by eliminating service level agreement failure cascading effects on violation detection. Copyright © 2011 John Wiley & Sons, Ltd.

Cloud computing paradigm allows on-demand access to computing and storages services over the Internet. To solve the complexity of application deployment in Cloud infrastructure, virtual appliances, pre-configured, ready-to-run applications are emerging as a breakthrough technology. However, an automated approach for deploying network of appliances is required to guarantee minimum deployment cost, low latency, and high reliability. In this paper, we propose and compare two different deployment approaches: Forward-checking-based backtracking (FCBB) and genetic-based. They take into account Quality of Service (QoS) criteria such as reliability, data communication cost, and latency between multiple Clouds to choose the most appropriate combination of virtual machines and appliances. We evaluate our approach using a real case study and different request types. Experimental results show both algorithms reach near optimal solution. Further, we investigate effects of factors such as latency requirements, and data communication between appliances on the performance of the algorithms and placement of appliances across multiple Clouds.

Cloud computing is a computing paradigm which allows access of computing elements and storages on-demand over the Internet. Virtual Appliances, pre-configured, ready-to-run applications are emerging as a breakthrough technology to solve the complexities of service deployment on Cloud infrastructure. However, an automated approach to deploy required appliances on the most suitable Cloud infrastructure is neglected by previous works which is the focus of this work. In this paper, we propose an effective architecture using ontology-based discovery to provide QoS aware deployment of appliances on Cloud service providers. In addition, we test our approach on a case study and the result shows the efficiency and effectiveness of the proposed work.

Automated composition of Semantic Web services has become one of the recent critical issues in today's Web environment. Despite the importance of AI-planning techniques for Web service composition, previous works in that area do not address security issues, which is the focus of this paper. Based on our prior work, i.e. AIMO, we present an approach called SCAIMO to achieve security conscious composition of Semantic Web services. Moreover, we propose a Secure Task Matchmaker which is responsible of matching security conscious tasks with operators and methods based on Description Logics and Web Service Modeling Ontology (WSMO). We test our approach on a case study and the result shows SCAIMO can provide an applicable solution.