Showing papers on "Elasticity (cloud computing) published in 2015"

Elasticity in cloud computing: a survey

Abstract: Cloud computing is now a well-consolidated paradigm for on-demand services provisioning on a pay-as-you-go model. Elasticity, one of the major benefits required for this computing model, is the ability to add and remove resources “on the fly” to handle the load variation. Although many works in literature have surveyed cloud computing and its features, there is a lack of a detailed analysis about elasticity for the cloud. As an attempt to fill this gap, we propose this survey on cloud computing elasticity based on an adaptation of a classic systematic review. We address different aspects of elasticity, such as definitions, metrics and tools for measuring, evaluation of the elasticity, and existing solutions. Finally, we present some open issues and future directions. To the best of our knowledge, this is the first study on cloud computing elasticity using a systematic review approach.

Wireless sensor and actuator system for smart irrigation on the cloud

Abstract: The number of devices connected to the Internet is experiencing an explosive growth. The interconnection of smart objects embedded with sensors enables them to interact with the environment and among themselves, forming a Wireless Sensor Network (WSN). These network nodes perform acquisition, collection and analysis of data, such as temperature and soil moisture. Such data can be employed to automate the irrigation process in agriculture while decreasing water consumption, resulting in monetary and environmental benefits. The high storage and processing capabilities, the rapid elasticity and payper-use characteristics makes Cloud Computing an attractive solution to the large amount of data generated by the WSN. This paper proposes and evaluates on a real deployment a cloud-based Wireless Sensor and Actuator Network (WSAN) communication system. This solution monitors and controls a set of sensors and actuators, respectively, to assess plants water needs.

Scalability, Elasticity, and Efficiency in Cloud Computing: a Systematic Literature Review of Definitions and Metrics

Abstract: Context: In cloud computing, there is a multitude of definitions and metrics for scalability, elasticity, and efficiency. However, stakeholders have little guidance for choosing fitting definitions and metrics for these quality properties, thus leading to potential misunderstandings. For example, cloud consumers and providers cannot negotiate reliable and quantitative service level objectives directly understood by each stakeholder. Objectives: Therefore, we examine existing definitions and metrics for these quality properties from the viewpoint of cloud consumers, cloud providers, and software architects with regard to commonly used concepts. Methods: We execute a systematic literature review (SLR), reproducibly collecting common concepts in definitions and metrics for scalability, elasticity, and efficiency. As quality selection criteria, we assess whether existing literature differentiates the three properties, exemplifies metrics, and considers typical cloud characteristics and cloud roles. Results: Our SLR yields 418 initial results from which we select 20 for in-depth evaluation based on our quality selection criteria. In our evaluation, we recommend concepts, definitions, and metrics for each property. Conclusions: Software architects can use our recommendations to analyze the quality of cloud computing applications. Cloud providers and cloud consumers can specify service level objectives based on our metric suggestions.

Scaling the LTE control-plane for future mobile access

Abstract: In addition to growth of data traffic, mobile networks are bracing for a significant rise in the control-plane signaling. While a complete re-design of the network to overcome inefficiencies may help alleviate the effects of signaling, our goal is to improve the design of the current platform to better manage the signaling. To meet our goal, we combine two key trends. Firstly, mobile operators are keen to transform their networks with the adoption of Network Function Virtualization (NFV) to ensure economies of scales. Secondly, growing popularity of cloud computing has led to advances in distributed systems. In bringing these trends together, we solve several challenges specific to the context of telecom networks. We present SCALE - A framework for effectively virtualizing the MME (Mobility Management Entity), a key control-plane element in LTE. SCALE is fully compatible with the 3GPP protocols, ensuring that it can be readily deployed in today's networks. SCALE enables (i) computational scaling with load and number of devices, and (ii) computational multiplexing across data centers, thereby reducing both, the latencies for control-plane processing, and the VM provisioning costs. Using an LTE prototype implementation and large-scale simulations, we show the efficacy of SCALE.

MDP and Machine Learning-Based Cost-Optimization of Dynamic Resource Allocation for Network Function Virtualization

Abstract: The introduction of Network Functions Virtualization (NFV) enables service providers to offer software-defined network functions with elasticity and flexibility. Its core technique, dynamic allocation procedure of NFV components onto cloud resources requires rapid response to changes on-demand to remain cost and QoS effective. In this paper, Markov Decision Process (MDP) is applied to the NP-hard problem to dynamically allocate cloud resources for NFV components. In addition, Bayesian learning method is applied to monitor the historical resource usage in order to predict future resource reliability. Experimental results show that our proposed strategy outperforms related approaches.

Aggressive Resource Provisioning for Ensuring QoS in Virtualized Environments

Abstract: Elasticity has now become the elemental feature of cloud computing as it enables the ability to dynamically add or remove virtual machine instances when workload changes. However, effective virtualized resource management is still one of the most challenging tasks. When the workload of a service increases rapidly, existing approaches cannot respond to the growing performance requirement efficiently because of either inaccuracy of adaptation decisions or the slow process of adjustments, both of which may result in insufficient resource provisioning. As a consequence, the Quality of Service (QoS) of the hosted applications may degrade and the Service Level Objective (SLO) will be thus violated. In this paper, we introduce SPRNT, a novel resource management framework, to ensure high-level QoS in the cloud computing system. SPRNT utilizes an aggressive resource provisioning strategy which encourages SPRNT to substantially increase the resource allocation in each adaptation cycle when workload increases. This strategy first provisions resources which are possibly more than actual demands, and then reduces the over-provisioned resources if needed. By applying the aggressive strategy, SPRNT can satisfy the increasing performance requirement in the first place so that the QoS can be kept at a high level. The experimental results show that SPRNT achieves up to 7.7 $\times$ speedup in adaptation time, compared with existing efforts. By enabling quick adaptation, SPRNT limits the SLO violation rate up to 1.3 percent even when dealing with rapidly increasing workload.

Resource Elasticity for Large-Scale Machine Learning

Abstract: Declarative large-scale machine learning (ML) aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations to distributed computations on MapReduce (MR) or similar frameworks. State-of-the-art compilers in this context are very sensitive to memory constraints of the master process and MR cluster configuration. Different memory configurations can lead to significant performance differences. Interestingly, resource negotiation frameworks like YARN allow us to explicitly request preferred resources including memory. This capability enables automatic resource elasticity, which is not just important for performance but also removes the need for a static cluster configuration, which is always a compromise in multi-tenancy environments. In this paper, we introduce a simple and robust approach to automatic resource elasticity for large-scale ML. This includes (1) a resource optimizer to find near-optimal memory configurations for a given ML program, and (2) dynamic plan migration to adapt memory configurations during runtime. These techniques adapt resources according to data, program, and cluster characteristics. Our experiments demonstrate significant improvements up to 21x without unnecessary over-provisioning and low optimization overhead.

Dynamically Scaling Apache Storm for the Analysis of Streaming Data

Abstract: Stream processing platforms allow applications to analyse incoming data continuously. Several use cases exist that make use of these capabilities, ranging from monitoring of physical infrastructures to pre selecting video surveillance feeds for human inspection. It is difficult to predict how much computing resources are needed for these stream processing platforms, because the volume and velocity of input data may vary over time. The open source Apache Storm software provides a framework for developers to build processing applications that use the computing resources of all machines within an established cluster. Because of the varying processing needs of such applications, the platform should be able to automatically grow and shrink as needed. Unfortunately, the current Storm platform does not provide this capability. In this paper we describe the design and implementation of a tool that monitors several aspects of the Storm platform, the applications running on top of it, and external systems such as queues and databases. Based on this information, the tool decides whether extra servers are needed or machines may be decommissioned from the cluster, and then acts upon it by actually creating new virtual machines or shutting them down.

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

Abstract: Auto-scaling features enable cloud applications to maintain enough resources to satisfy demand spikes, reduce costs and keep performance in check. Most auto-scaling strategies rely on a predefined set of rules to scale up/down the required resources depending on the application usage. Those rules are however difficult to devise and generalize, and users are often left alone tuning auto-scale parameters of essentially blackbox applications. In this paper, we propose a novel fuzzy reinforcement learning controller, FQL4KE, which automatically scales up or down resources to meet performance requirements. The Q-Learning technique, a model-free reinforcement learning strategy, frees users of most tuning parameters. FQL4KE has been successfully applied and we therefore think that a fuzzy controller with Q-Learning is indeed a promising combination for auto-scaling resources.

Dependable horizontal scaling based on probabilistic model checking

Abstract: The focus of this work is the on-demand resource provisioning in cloud computing, which is commonly referred to as cloud elasticity. Although a lot of effort has been invested in developing systems and mechanisms that enable elasticity, the elasticity decision policies tend to be designed without quantifying or guaranteeing the quality of their operation. We present an approach towards the development of more formalized and dependable elasticity policies. We make two distinct contributions. First, we propose an extensible approach to enforcing elasticity through the dynamic instantiation and online quantitative verification of Markov Decision Processes (MDP) using probabilistic model checking. Second, various concrete elasticity models and elasticity policies are studied. We evaluate the decision policies using traces from a real NoSQL database cluster under constantly evolving external load. We reason about the behaviour of different modeling and elasticity policy options and we show that our proposal can improve upon the state-of-the-art in significantly decreasing under-provisioning while avoiding over-provisioning.

Systematically Deriving Quality Metrics for Cloud Computing Systems

Abstract: In cloud computing, software architects develop systems for virtually unlimited resources that cloud providers account on a pay-per-use basis. Elasticity management systems provision these resources autonomously to deal with changing workload. Such changing workloads call for new objective metrics allowing architects to quantify quality properties like scalability, elasticity, and efficiency, e.g., for requirements/SLO engineering and software design analysis. In literature, initial metrics for these properties have been proposed. However, current metrics lack a systematic derivation and assume knowledge of implementation details like resource handling. Therefore, these metrics are inapplicable where such knowledge is unavailable.To cope with these lacks, this short paper derives metrics for scalability, elasticity, and efficiency properties of cloud computing systems using the goal question metric (GQM) method. Our derivation uses a running example that outlines characteristics of cloud computing systems. Eventually, this example allows us to set up a systematic GQM plan and to derive an initial set of six new metrics. We particularly show that our GQM plan allows to classify existing metrics.

Cloud Forensics: A Review of Challenges, Solutions and Open Problems

Abstract: Cloud computing is a promising next generation computing paradigm which offers significant economic benefits to both commercial and public entities. Due to the unique combination of characteristics that cloud computing introduce, including; on-demand self-service, broad network access, resource pooling, rapid elasticity and measured service, digital investigations face various technical, legal and organizational challenges to keep up with current developments in the field of cloud computing. There are plenty of issues that need to be resolved in order to perform a proper digital investigation in the cloud environment. This paper examines the challenges in cloud forensics that are identified in the current research literature. Furthermore it explores the current research proposals and technical solutions addressed in the respective research. Ultimately, it highlights the open problems that need further efforts to be tackled.

Cloud based mobile device management systems and methods

Abstract: The present disclosure relates to cloud based mobile device management (MDM) systems and methods to use the “cloud” to pervasively manage mobile devices. The cloud based MDM systems and methods provide an ability to manage mobile devices with or without MDM clients while no requiring an MDM appliance or service at the enterprise. This provides a “no hardware, no software” deployment. In an exemplary embodiment, a client-less implementation leverages the ActiveSync protocol proxied through distributed cloud nodes to enforce mobile policies. In another exemplary embodiment, a client-based implementation uses a platform specific application and associated application programming interfaces (API) to connect managed mobile devices and provide MDM features through the cloud. Advantageously, the cloud based MDM systems and methods provide reliability and resiliency, elasticity, lower cost, mobility, integration of management and security, and agility over conventional MDM based solutions.

Coordinating CPU and Memory Elasticity Controllers to Meet Service Response Time Constraints

Abstract: Vertical elasticity is recognized as a key enabler for efficient resource utilization of cloud infrastructure through fine-grained resource provisioning, e.g., allowing CPU cycles to be leased for as short as a few seconds. However, little research has been done to support vertical elasticity where the focus is mostly on a single resource, either CPU or memory, while an application may need arbitrary combinations of these resources at different stages of its execution. Nonetheless, the existing techniques cannot be readily used as-is without proper orchestration since they may lead to either under-or over-provisioning of resources and consequently result in undesirable behaviors such as performance disparity. The contribution of this paper is the design of an autonomic resource controller using a fuzzy control approach as a coordination technique. The novel controller dynamically adjusts the right amount of CPU and memory required to meet the performance objective of an application, namely its response time. We perform a thorough experimental evaluation using three different interactive benchmark applications, RUBiS, RUBBoS, and Olio, under workload traces generated based on open and closed system models. The results show that the coordination of memory and CPU elasticity controllers using the proposed fuzzy control provisions the right amount of resources to meet the response time target without over-committing any of the resource types. In contrast, with no coordinating between controllers, the behaviour of the system is unpredictable e.g., the application performance may be met but at the expense of over-provisioning of one of the resources, or application crashing due to severe resource shortage as a result of conflicting decisions.

A hybrid heuristic-based tuned support vector regression model for cloud load prediction

Abstract: Cloud computing elasticity helps the cloud providers to handle large amount of computation and storage demands in an efficient manner. Proactively provisioning cloud workload is essential in order to keep the cloud utilization and service-level agreement at an acceptable level. Problems such as new virtual machine start-up latency, energy minimization and efficient resource provisioning, requires to predict resource demands for a few minutes ahead. Since the Cloud workloads have a very dynamic nature, CPU/memory usage varies considerably in the cloud. Also, existing prediction methods have considerable prediction error and erroneous results. So we propose a novel tuned support vector regression (TSVR) scheme that carefully selects three SVR parameters by a hybrid genetic algorithm and particle swarm optimization method. A chaotic sequence is devised into the algorithm to improve prediction accuracy and simultaneously avoid premature converging. To demonstrate the prediction accuracy of our TSVR model, we conduct a simulation study using Google cloud traces. The simulation results show that the proposed TSVR model achieves better prediction performance than conventional models in terms of standard metrics.

Space-Time Aware Behavioral Topic Modeling for Microblog Posts

Abstract: Off-the-Shelf (OTS), relational databases can be challenging when deployed in the cloud. Given their architectures, limitations arise regarding performance (because of virtualization), scalability (because of multi-tenancy), and elasticity (because existing engines cannot easily take advantage of the application migration possibilities of virtualized environments). As a result, many database engines tailored to cloud computing differ from conventional engines in functionality, consistency levels, support for queries or transactions, and even interfaces. Efficiently supporting Off-the-Shelf databases in the cloud would allow to port entire application stacks without rewriting them. In this paper we present a system that combines snapshot isolation (SI) replication with virtualization to provide a flexible solution for running unmodified databases in the cloud while taking advantage of the opportunities cloud architectures provide. Unlike replication-only solutions, our system works well both within larger servers and across clusters. Unlike virtualization only solutions, our system provides better performance and more flexibility in the deployment.

Dynamic combination of improved max-min and ant colony algorithm for load balancing in cloud system

Abstract: Cloud Computing is a new technology. Cloud Computing provides many facilities like on demand self-services, unlimited resources, rapid elasticity and measured services to end users. All users access these resources directly through internet. Users can use these resources and services as they want on pay per use concept. In cloud computing architecture load balancing is a very important issue. There are many algorithms for load balancing in cloud computing. All algorithms work different ways. We proposed a Improved Max-Min Ant colony Algorithm. Improved Max-Min used the concept of original Max-Min. Improved Max-Min is based on the execution time not on completion time as a selection basis. The main motive of our work is to balance the total load of cloud system .We try to minimizing the total makespan. We simulated results using the CloudSim toolkit. Results show the comparison between improved max min and new hybrid improved Max-Min ant approach. It mainly focuses on total processing time and processing cost.

Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework

Abstract: Cloud computing provides on-demand access to affordable hardware (multi-core CPUs, GPUs, disks, and networking equipment) and software (databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single or multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS:Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers, spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

Improving Agility and Elasticity in Bare-metal Clouds

Abstract: Bare-metal clouds are an emerging infrastructure-as-a-service (IaaS) that leases physical machines (bare-metal instances) rather than virtual machines, allowing resource-intensive applications to have exclusive access to physical hardware. Unfortunately, bare-metal instances require time-consuming or OS-specific tasks for deployment due to the lack of virtualization layers, thereby sacrificing several beneficial features of traditional IaaS clouds such as agility, elasticity, and OS transparency. We present BMcast, an OS deployment system with a special-purpose de-virtualizable virtual machine monitor (VMM) that supports quick and OS-transparent startup of bare-metal instances. BMcast performs streaming OS deployment while allowing direct access to physical hardware from the guest OS, and then disappears after completing the deployment. Quick startup of instances improves agility and elasticity significantly, and OS transparency greatly simplifies management tasks for cloud customers. Experimental results have confirmed that BMcast initiated a bare-metal instance 8.6 times faster than image copying, and database performance on BMcast during streaming OS deployment was comparable to that on a state-of-the-art VMM without performing deployment. BMcast incurred zero overhead after de-virtualization.

e-Health cloud implementation issues and efforts

Abstract: The healthcare industry is facing several challenges and significant pressure to decrease the costs associated with providing healthcare services, adopt new electronic healthcare systems, and communicate data quickly and securely with other healthcare and government agencies. Recently, the healthcare industry started perceiving cloud computing as a solution for its elasticity, scalability and universal access to the medical data anywhere and anytime needs. This paper illustrates the concept of electronic healthcare (e-Health), highlights the technical and non-technical issues of migrating to cloud computing solutions. Furthermore, the paper illustrates different solutions of cloud-based e-Health implementations to compare these efforts with the current issues.

HeteroVisor: Exploiting Resource Heterogeneity to Enhance the Elasticity of Cloud Platforms

Abstract: This paper presents HeteroVisor, a heterogeneity-aware hypervisor, that exploits resource heterogeneity to enhance the elasticity of cloud systems. Introducing the notion of 'elasticity' (E) states, HeteroVisor permits applications to manage their changes in resource requirements as state transitions that implicitly move their execution among heterogeneous platform components. Masking the details of platform heterogeneity from virtual machines, the E-state abstraction allows applications to adapt their resource usage in a fine-grained manner via VM-specific 'elasticity drivers' encoding VM-desired policies. The approach is explored for the heterogeneous processor and memory subsystems evolving for modern server platforms, leading to mechanisms that can manage these heterogeneous resources dynamically and as required by the different VMs being run. HeteroVisor is implemented for the Xen hypervisor, with mechanisms that go beyond core scaling to also deal with memory resources, via the online detection of hot memory pages and transparent page migration. Evaluation on an emulated heterogeneous platform uses workload traces from real-world data, demonstrating the ability to provide high on-demand performance while also reducing resource usage for these workloads.

Cloud-Based Multimedia Content Protection System

Abstract: We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including 2-D videos, 3-D videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of 3-D videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of 3-D videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 3-D videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of 3-D videos, while our system detects more than 98% of them. This comparison shows the need for the proposed 3-D signature method, since the state-of-the-art commercial system was not able to handle 3-D videos.

Energy Efficient Multi Dimensional Host Load Aware Algorithm for Virtual Machine Placement and Optimization in Cloud Environment

Abstract: The effectiveness and elasticity of virtual machine placement has become a main concern in modern cloud computing environment. Mapping the virtual machines to the physical machines cluster is called the VM placement. In this paper we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We used two different techniques, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. The presented algorithm is implemented in JAVA Net beans IDE, and Clouds simulator has been used for simulation to assess the execution and performance of our heuristics by comparison with algorithms first fit, best fit and round robin. The performance of the proposed algorithm was examined from both users and service provider’s perception. The simulation results show that our proposed algorithm uses the less number of physical servers for placing a certain number of VMs which helps to improve the resource utilization rate. The response time of our algorithm is little bit more than the first fit algorithm because of its nature of allocating VMs is based on the user constraints and past usage history of the VMs. Elevated SLA satisfaction rate and inferior load imbalance rate was observed in results. Since we used a modified version of hybrid genetic algorithm for load optimization the percentage of VM migrations had been decreased through which we can achieve the better results for load balancing along with cost reduction. The results also show that our hybrid genetic based multi dimensional host load aware and user constraints based algorithm is applicable, valuable and reliable for implementation in real data center environments.

An empirical study on cloud computing requirements for better mobile learning services

Abstract: Information technology and its applications change not only the way students learn but also how they are taught. M-learning provides many advantages to our education system. Cloud computing is the new technology that has started to be widely adopted for use with several applications by many education providers. A number of issues that have delayed the complete adoption of M-learning applications through cloud computing services still need to be solved. This paper defines an appropriate set of requirements, i.e. availability, quick response, flexibility and ease of use, long-term storage, elasticity and scalability, integrity, privacy and confidentiality and the control of information flow. The dimensions of these requirements are tailored to criteria developed from the literature study, standards for software quality and their guidelines. The paper then highlights the level of importance of each defined requirement for higher educational institutions, training centres, research laboratories, infant and junior schools.

The Internet of Things Resource Management Challenge

Abstract: Caused by the proliferation of the (IoT) and its related application domains such as Building Automation or E-Health, users face a continuously increasing amount of heterogeneous sensors and devices deployed to their environment. As a result, a large variety of protocols, data formats and physical sensing resources needs to be managed in order to gain benefit from the deployed devices. This raises the question how the resources provided by the devices can be efficiently managed and provisioned. Related concepts like on-demand provisioning, elasticity, or resource pooling and sharing are already known from the Cloud Computing domain. This paper presents the Device Cloud approach, which aims at mitigating the IoT resource management issues by applying Cloud Computing concepts to the IoT domain. Similar to the Pay-as-you-Go paradigm, the Device Cloud allows users to allocate devices from a shared resource pool on-demand. Sensors and devices are not just integrated with the Cloud by being enabled to utilize Cloud services. Instead, the physical (IoT) resources become an integral part of the Cloud resource pool and are shared and provisioned like regular Infrastructure as a Service (IaaS) Cloud resources.

Malware detection in the cloud under Ensemble Empirical Mode Decomposition

Abstract: Cloud networks underpin most of todays' socio-economical Information Communication Technology (ICT) environments due to their intrinsic capabilities such as elasticity and service transparency. Undoubtedly, this increased dependence of numerous always-on services with the cloud is also subject to a number of security threats. An emerging critical aspect is related with the adequate identification and detection of malware. In the majority of cases, malware is the first building block for larger security threats such as distributed denial of service attacks (e.g. DDoS); thus its immediate detection is of crucial importance. In this paper we introduce a malware detection technique based on Ensemble Empirical Mode Decomposition (E-EMD) which is performed on the hypervisor level and jointly considers system and network information from every Virtual Machine (VM). Under two pragmatic cloud-specific scenarios instrumented in our controlled experimental testbed we show that our proposed technique can reach detection accuracy rates over 90% for a range of malware samples. In parallel we demonstrate the superiority of the introduced approach after comparison with a covariance-based anomaly detection technique that has been broadly used in previous studies. Consequently, we argue that our presented scheme provides a promising foundation towards the efficient detection of malware in modern virtualized cloud environments.

Mobility and bandwidth prediction as a service in virtualized LTE systems

Abstract: Recently telecommunication industry benefits from infrastructure sharing, one of the most fundamental enablers of cloud computing, leading to emergence of the Mobile Virtual Network Operator (MVNO) concept. The most momentous intents by this approach are the support of on-demand provisioning and elasticity of virtualized mobile network components, based on data traffic load. To realize it, during operation and management procedures, the virtualized services need be triggered in order to scale-up/down or scale-out/in an service instance. In this paper we propose an architecture called MOBaaS (Mobility and Bandwidth Availability Prediction as a Service), comprising two algorithms in order to predict user(s) mobility and network link bandwidth availability, that can be implemented in cloud based mobile network structure and can be used as a support service by any other virtualized mobile network service. MOBaaS can provide prediction information in order to generate required triggers for on-demand deploying, provisioning, disposing of virtualized network components. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operation, as well. Through the preliminary experiments with the prototype implementation on the OpenStack platform, we evaluated and confirmed the feasibility and the effectiveness of the prediction algorithms and the proposed architecture.

Performance-based service differentiation in clouds

Abstract: Due to fierce competition, cloud providers need to run their data-centers efficiently. One of the issues is to increase data-center utilization while maintaining applications' performance targets. Achieving high data-center utilization while meeting applications' performance is difficult, as data-center overload may lead to poor performance of hosted services. Service differentiation has been proposed to control which services get degraded. However, current approaches are capacity-based, which are oblivious to the observed performance of each service and cannot divide the available capacity among hosted services so as to minimize overall performance degradation. In this paper we propose performance-based service differentiation. In case enough capacity is available, each service is automatically allocated the right amount of capacity that meets its target performance, expressed either as response time or throughput. In case of overload, we propose two service differentiation schemes that dynamically decide which services to degrade and to what extent. We carried out an extensive set of experiments using different services -- interactive as well as non-interactive -- by varying the workload mixes of each service over time. The results demonstrate that our solution precisely provides guaranteed performance or service differentiation depending on available capacity.

Cloud Computing Design Patterns

Abstract: This book continues the very high standard we have come toexpect from ServiceTech Press. The book provideswell-explainedvendor-agnostic patterns to the challenges of providing or using cloud solutions from PaaS to SaaS. The book is not only a great patterns reference, but also worth reading from cover to cover as the patterns arethought-provoking,drawing out points that you should consider and ask of a potential vendor if youre adopting a cloud solution.--Phil Wilkins, Enterprise Integration Architect, Specsavers Thomas Erls text provides a unique and comprehensive perspective on cloud design patterns that is clearly and concisely explained for the technical professional and layman alike. It is an informative, knowledgeable, and powerful insight that may guide cloud experts in achieving extraordinary results based on extraordinary expertise identified in this text. I will use this text as a resource in future cloud designs and architectural considerations.--Dr. Nancy M. Landreville, CEO/CISO, NML Computer Consulting The Definitive Guide to Cloud Architecture and Design Best-selling service technology author Thomas Erl has brought together the de facto catalog of design patterns for modern cloud-based architecture and solution design. More than two years in development, this books 100+ patterns illustrate proven solutions to common cloud challenges and requirements. Its patterns are supported by rich, visual documentation, including 300+ diagrams. The authors address topics covering scalability, elasticity, reliability, resiliency, recovery, data management, storage, virtualization, monitoring, provisioning, administration, and much more. Readers will further find detailed coverage of cloud security, from networking and storage safeguards to identity systems, trust assurance, and auditing. This books unprecedented technical depth makes it a must-have resource for every cloud technology architect, solution designer, developer, administrator, and manager. Topic Areas Enabling ubiquitous, on-demand, scalable network access to shared pools of configurable IT resources Optimizing multitenant environments to efficiently serve multiple unpredictable consumers Using elasticity best practices to scale IT resources transparently and automatically Ensuring runtime reliability, operational resiliency, and automated recovery from any failure Establishing resilient cloud architectures that act as pillars for enterprise cloud solutions Rapidly provisioning cloud storage devices, resources, and data with minimal management effort Enabling customers to configure and operate custom virtual networks in SaaS, PaaS, or IaaS environments Efficiently provisioning resources, monitoring runtimes, and handling day-to-day administration Implementing best-practice security controls for cloud service architectures and cloud storage Securing on-premise Internet access, external cloud connections, and scaled VMs Protecting cloud services against denial-of-service attacks and traffic hijacking Establishing cloud authentication gateways, federated cloud authentication, and cloud key management Providing trust attestation services to customers Monitoring and independently auditing cloud security Solving complex cloud design problems with compound super-patterns