A survey of fault tolerance in cloud computing

Cloud computing has brought about a transformation in the delivery model of information technology from a product to a service. It has enabled the availability of various software, platforms and infrastructural resources as scalable services on demand over the internet. However, the performance of cloud computing services is hampered due to their inherent vulnerability to failures owing to the scale at which they operate. It is possible to utilize cloud computing services to their maximum potential only if the performance related issues of reliability, availability, and throughput are handled effectively by cloud service providers. Therefore, fault tolerance becomes a critical requirement for achieving high performance in cloud computing. This paper presents a comprehensive overview of fault tolerance-related issues in cloud computing; emphasizing upon the significant concepts, architectural details, and the state-of-art techniques and methods. The objective is to provide insights into the existing fault tolerance approaches as well as challenges yet required to be overcome. The survey enumerates a few promising techniques that may be used for efficient solutions and also, identifies important research directions in this area.

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title: An Efficient Optimistic Message Logging Scheme for Recoverable Mobile Computing Systems
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title: A Communication-Induced Checkpointing and Asynchronous Recovery Protocol for Mobile Computing Systems
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title: A Checkpointing Algorithm for Mobile Computing Environment
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title: Low-Cost Checkpointing and Failure Recovery in Mobile Computing Systems
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title: Distributed snapshots for mobile computing systems
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title: Fault Tolerance Techniques in Grid Computing Systems
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title: Adaptive checkpointing for fault tolerance in an autonomous mobile computing grid
abstract: The widespread availability and increasing processing power of mobile devices has lead to a focus towards the development of autonomous mobile computing grids (MoGs). Such mobile grids allow the successful execution of distributed applications; without access to any static nodes or wired networks. However, the implementation of a fault tolerance technique is essential to completely utilize the mobile devices as viable computing resources. Checkpointing is a well explored fault tolerance technique for mobile computing systems. The paper presents an adaptive checkpointing technique for failure recovery of mobile nodes in a MoG. The presented protocol relies on cooperative checkpointing by the constituent nodes in the system. A node uses the stable storage of other nodes in the system to save its checkpoint data, in case the requisite stable storage is not available at the node itself. Further, depending on the availability of resources in the MoG, the scheme replicates a node's checkpoint data at multiple nodes. The results of simulation for the presented scheme verify that the introduction of redundancy with the checkpointing procedure vastly increases the likelihood of successful recovery of a failed node in a MoG.

title: Decentralized QoS-Aware Checkpointing Arrangement in Mobile Grid Computing
abstract: This paper deals with decentralized, QoS-aware middleware for checkpointing arrangement in Mobile Grid (MoG) computing systems. Checkpointing is more crucial in MoG systems than in their conventional wired counterparts due to host mobility, dynamicity, less reliable wireless links, frequent disconnections, and variations in mobile systems. We've determined the globally optimal checkpoint arrangement to be NP-complete and so consider Reliability Driven (ReD) middleware, employing decentralized QoS-aware heuristics, to construct superior checkpointing arrangements efficiently. With ReD, an MH (mobile host) simply sends its checkpointed data to one selected neighboring MH, and also serves as a stable point of storage for checkpointed data received from a single approved neighboring MH. ReD works to maximize the probability of checkpointed data recovery during job execution, increasing the likelihood that a distributed application, executed on the MoG, completes without sustaining an unrecoverable failure. It allows collaborative services to be offered practically and autonomously by the MoG. Simulations and actual testbed implementation show ReD's favorable recovery probabilities with respect to Random Checkpointing Arrangement (RCA) middleware, a QoS-blind comparison protocol producing random arbitrary checkpointing arrangements.

title: Movement-Based Checkpointing and Message Logging for Recovery in MANETs
abstract: Mobile ad hoc networks (MANETs) are increasingly being employed for expanding the computing capabilities of existing cellular mobile systems and in the implementation of mobile computing grids. However, MANETs are susceptible to various transient as well as permanent failures and a fault tolerance technique is crucial in order to effectively utilize the constituent nodes as viable compute resources. Checkpointing and message logging based rollback recovery is a well established approach to provide fault tolerance in static and cellular mobile distributed systems; yet its use for achieving fault tolerance in MANETs is comparatively less explored. The existing recovery algorithms cannot be applied directly to MANETs due to their insufficiency in handling challenges like absence of static infrastructure, frequent node movement, constrained wireless bandwidth and limited stable storage. In this paper, we propose a checkpointing based rollback recovery protocol for clustered MANETs that determines the checkpointing frequency of a mobile node based on its mobility; thereby avoiding unnecessary checkpoints. The protocol uses a popular graph theoretic construct called connected dominating set to lower the communication overhead due to the recovery procedure. The findings of our scheme have been substantiated by the complexity analysis and simulation under varying network conditions.

title: Fault Tolerance QoS Routing Protocol for MANETs
abstract: A Mobile Ad Hoc networks is a dynamic environment which due to frequently mobile wireless nodes experiences Communication failures due to network partitioning, and nodes failures exhibiting different faulty actions temporary or long lasting arising out of glitches related to hardware or software. As the mobile nodes are mostly resource constrained, in case of faulty nodes packets forwarding could be lead to further complications. Hence in designing a robust mobile ad hoc network fault tolerance plays a major role. Due to the presence of faulty nodes, the Performance of routing degrades and the reason for the faulty nodes has to be identified to address routing by exploring network redundancies. In our previous work[13],we devised Genetic Algorithm(GA) based Energy efficient QoS routing (GAEEQR) protocol. As extension of previous work, we devised protocol called Fault Tolerance QoS Routing Protocol, which has capability to send the data with an alternative route when route break occurs. This protocol gives the better results than GAEEQR in terms of delay, packet delivery ratio, and throughput and energy consumption.

title: A Survey of Data Center Network Architectures
abstract: Large-scale data centers form the core infrastructure support for the ever expanding cloud based services. Thus the performance and dependability characteristics of data centers will have significant impact on the scalability of these services. In particular, the data center network needs to be agile and reconfigurable in order to respond quickly to ever changing application demands and service requirements. Significant research work has been done on designing the data center network topologies in order to improve the performance of data centers. In this paper, we present a survey of data center network designs and topologies that have published recently. We start with a discussion on various representative data center network topologies, and compare them with respect to several properties in order to highlight their advantages and disadvantages. Thereafter, we discuss several routing protocols designed for these topologies, and compare them based on various criteria: the basic algorithms to establish connections, the techniques used to gain better performance and the mechanisms for faulttolerance. A good understanding of the state-of-the-art in data center networks would enable the design of future architectures in order to improve performance and dependability of data centers.

title: Rethinking the Data Center Networking: Architecture, Network Protocols, and Resource Sharing
abstract: Large-scale data centers enable the new era of cloud computing and provide the core infrastructure to meet the computing and storage requirements for both enterprise information technology needs and cloud-based services. To support the ever-growing cloud computing needs, the number of servers in today's data centers are increasing exponentially, which in turn leads to enormous challenges in designing an efficient and cost-effective data center network. With data availability and security at stake, the issues with data center networks are more critical than ever. Motivated by these challenges and critical issues, many novel and creative research works have been proposed in recent years. In this paper, we investigate in data center networks and provide a general overview and analysis of the literature covering various research areas, including data center network interconnection architectures, network protocols for data center networks, and network resource sharing in multitenant cloud data centers. We start with an overview on data center networks and together with its requirements navigate the data center network designs. We then present the research literature related to the aforementioned research topics in the subsequent sections. Finally, we draw the conclusions.

title: A survey on data center networking for cloud computing
abstract: Data Center Networks (DCNs) are an essential infrastructure that impact the success of cloud computing. A scalable and efficient data center is crucial in both the construction and operation of stable cloud services. In recent years, the growing importance of data center networking has drawn much attention to related issues including connective simplification and service stability. However, existing DCNs lack the necessary agility for multi-tenant demands in the cloud, creating poor responsiveness and limited scalability. In this paper, we present an overview of data center networks for cloud computing and evaluate construction prototypes based on these issues. We provide, specifically, detailed descriptions of several important aspects: the physical architecture, virtualized infrastructure, and DCN routing. Each section of this work discusses and evaluates resolution approaches, and presents the use cases for cloud computing service. In our attempt to build insight relevant to future research, we also present some open research issues. Based on experience gained in both research and industrial trials, the future of data center networking must include careful consideration of the interactions between the important aspects mentioned above.

title: Network Structure or Topology
abstract: The geometrical arrangement of computer resources, remote devices and communication facilities is known as Network structure or Network topology.

title: A Study And Analysis on Computer Network Topology For Data Communication
abstract: In recent days for computing, distributed computer systems have become very important and popular issue. It delivers high end performance at a low cost. Autonomous computers are connected by means of a communication network in a distributed computing environment which is arranged in a geometrical shape called network topology. In the present paper a detailed study and analysis on network topologies is presented. Definitions of Physical and Logical Topologies are also provided.

title: The Impact of Network Topologies on the Performance of the In-Vehicle Network
abstract: Today's vehicles contain hundreds of circuits, sensors and around 80-120 Electronic Control Units(ECUs). The communication is needed among many circuits and functions of a vehicle. In earlier vehicle systems, this type of communication was handled via a dedicated wire through point-to-point connections. If all possible combinations of switches, sensors, ECUs and other electronic devices in fully featured vehicles are accumulated, the resulting number of connections and dedicated wiring is enormous. Hence networking of these components is necessary to reduce the complexity of electronics inside a vehicle. In-vehicle networking provides a more efficient method for today's complex in-vehicle communications. This paper focuses on the comparison of the performance of ring and star network topologies on the basis of bus load.

title: A Dynamic and Compact Fault-Tolerant Strategy for Fat-tree ∗
abstract: High-performance interconnection networks are used to ach ieve the maximum performance in clusters of PCs. The fat-tree topology has raised in popularity in the last fe w y ars. Routing and fault-tolerance are two important design issues of these networks. This paper presents a dynam ic fault-tolerant routing strategy for fat-trees without additional hardware, supporting a reasonable number of fau lts. After a fault, an algorithm dynamically spreads the routing information, with a small impact on global system th roughput. The proposal is based on enhancing the Interval Routing scheme with exclusion intervals to indica te the nodes that become unreachable after a fault appears.

title: Efficient and Scalable Hardware-Based Multicast in Fat-Tree Networks
abstract: This article presents an efficient and scalable mechanism to overcome the limitations of collective communication in switched interconnection networks in the presence of faults. Considering that current trends in supercomputing are moving toward massively parallel computers, with many thousands of components, reliability becomes a challenge. In such scenario, fat-tree networks that provide hardware support for collective communication suffer from serious performance degradation due to the presence of, even, a single faulty node. This paper describes a new mechanism to provide high-performance collective communication in such situations. The feasibility of the proposed technique is formally demonstrated. We present the design of a new hardware-based routing algorithm for multicast, that is at the base of our proposal. The proposed mechanism is implemented and experimentally evaluated. Our experimental results show that hardware-based multicast trees provide an efficient and scalable solution for collective communication in fat-tree networks, significantly outperforming traditional solutions.

title: Dynamic Fault Tolerance in Fat Trees
abstract: Fat trees are a very common communication architecture in current large-scale parallel computers. The probability of failure in these systems increases with the number of components. We present a routing method for deterministically and adaptively routed fat trees, applicable to both distributed and source routing, that is able to handle several concurrent faults and that transparently returns to the original routing strategy once the faulty components have recovered. The method is local and dynamic, completely masking the fault from the rest of the system. It only requires a small extra functionality in the switches to handle rerouting packets around a fault. The method guarantees connectedness and deadlock and livelock freedom for up to k -1 benign simultaneous switch and/or link faults where k is half the number of ports in the switches. Our simulation experiments show a graceful degradation of performance as more faults occur. Furthermore, we demonstrate that for most fault combinations, our method will even be able to handle significantly more faults beyond the k -1 limit with high probability.

title: FT-RUFT: A Performance and Fault-Tolerant Efficient Indirect Topology
abstract: Although performance is a key design issue of interconnection networks, fault-tolerance is becoming more important due to the large amount of components of large machines. In this paper, we focus on designing a simple indirect topology with both good performance and fault-tolerance properties. The idea is to take full advantage of the network resources consumed by the topology. To do that, starting from the RUFT topology, which is a simple UMIN topology that does not tolerate any link fault, we first duplicate injection and ejection links connecting these extra links in a particular way. The resulting topology tolerates 3 network link faults and also slightly increases performance with marginal increase in the network hardware cost. Most important, contrary to most of the available topologies, the topology is able to tolerate also faults in the links that connect to end-nodes. We also propose another topology that also duplicates network links, achieving 2x performance improvements and tolerating up to 7 network link faults. These results are better than the ones obtained by a BMIN with a similar amount of resources.

title: RUFT: Simplifying the Fat-Tree Topology
abstract: The fat-tree is one of the most widely-used topologies by interconnection network manufacturers. Recently, a deterministic routing algorithm that optimally balances the network traffic in fat--trees was proposed. It can not only achieve almost the same performance than adaptive routing, but also outperforms it for some traffic patterns. Nevertheless, fat-trees require a high number of switches with a non-negligible wiring complexity. In this paper, we propose replacing the fat-tree by an unidirectional multistage interconnection network referred to as reduced unidirectional fat-tree (RUFT) that uses a a simplified version of the aforementioned deterministic routing algorithm. As a consequence, switch hardware is almost reduced to the half, decreasing, in this way, power consumption, arbitration complexity, switch size, and network cost. Evaluation results show that RUFT obtains lower latency than fat-tree for low and medium traffic loads. Furthermore, in large networks, it obtains almost the same throughput than the classical fat-tree.

title: Routing and Fault Tolerance in Z-Fat Tree
abstract: Fat tree topologies have been extensively used as interconnection networks for high performance computing, cluster and data center systems, with their most recent variants able to fairly extend and scale to accommodate higher processing power. While each progressive and evolved fat-tree topology includes some extra advancements, these networks do not fully address all the issues of large scale HPC. We propose a topology called Zoned-Fat tree ( Z-Fat tree,) which is a further extension to the fat trees. The extension relates to the provision of extra degree of connectivity to utilize the extra ports per switches (routing nodes), that are, in some cases, not utilized by the architectural constraints of other variants of fat trees, and hence increases the bisection bandwidth, reduces the latency and supplies additional paths for fault tolerance. To support and profit from the extra links, we propose an adaptive low latency routing for up traffic which is based on a series of leading direction bits predefined at the source; furthermore we suggest a deterministic routing by implementing a dynamic round robin algorithm that overtakes D-mod-K in same cases and guarantees the utilization of all the extra links. We also propose a fault tolerance algorithm, named recoil-and-reroute which makes use of the extra links to ensure higher message delivery even in the presence of faulty links and switches.

title: VL2: a scalable and flexible data center network
abstract: To be agile and cost effective, data centers should allow dynamic resource allocation across large server pools. In particular, the data center network should enable any server to be assigned to any service. To meet these goals, we present VL2, a practical network architecture that scales to support huge data centers with uniform high capacity between servers, performance isolation between services, and Ethernet layer-2 semantics. VL2 uses (1) flat addressing to allow service instances to be placed anywhere in the network, (2) Valiant Load Balancing to spread traffic uniformly across network paths, and (3) end-system based address resolution to scale to large server pools, without introducing complexity to the network control plane. VL2's design is driven by detailed measurements of traffic and fault data from a large operational cloud service provider. VL2's implementation leverages proven network technologies, already available at low cost in high-speed hardware implementations, to build a scalable and reliable network architecture. As a result, VL2 networks can be deployed today, and we have built a working prototype. We evaluate the merits of the VL2 design using measurement, analysis, and experiments. Our VL2 prototype shuffles 2.7 TB of data among 75 servers in 395 seconds - sustaining a rate that is 94% of the maximum possible.

title: A Survey and Evaluation of Data Center Network Topologies
abstract: Data centers are becoming increasingly popular for their flexibility and processing capabilities in the modern computing environment. They are managed by a single entity (administrator) and allow dynamic resource provisioning, performance optimization as well as efficient utilization of available resources. Each data center consists of massive compute, network and storage resources connected with physical wires. The large scale nature of data centers requires careful planning of compute, storage, network nodes, interconnection as well as inter-communication for their effective and efficient operations. In this paper, we present a comprehensive survey and taxonomy of network topologies either used in commercial data centers, or proposed by researchers working in this space. We also compare and evaluate some of those topologies using mininet as well as gem5 simulator for different traffic patterns, based on various metrics including throughput, latency and bisection bandwidth.

title: Dcell: a scalable and fault-tolerant network structure for data centers
abstract: A fundamental challenge in data center networking is how to efficiently interconnect an exponentially increasing number of servers. This paper presents DCell, a novel network structure that has many desirable features for data center networking. DCell is a recursively defined structure, in which a high-level DCell is constructed from many low-level DCells and DCells at the same level are fully connected with one another. DCell scales doubly exponentially as the node degree increases. DCell is fault tolerant since it does not have single point of failure and its distributed fault-tolerant routing protocol performs near shortest-path routing even in the presence of severe link or node failures. DCell also provides higher network capacity than the traditional tree-based structure for various types of services. Furthermore, DCell can be incrementally expanded and a partial DCell provides the same appealing features. Results from theoretical analysis, simulations, and experiments show that DCell is a viable interconnection structure for data centers.

title: BCube: a high performance, server-centric network architecture for modular data centers
abstract: This paper presents BCube, a new network architecture specifically designed for shipping-container based, modular data centers. At the core of the BCube architecture is its server-centric network structure, where servers with multiple network ports connect to multiple layers of COTS (commodity off-the-shelf) mini-switches. Servers act as not only end hosts, but also relay nodes for each other. BCube supports various bandwidth-intensive applications by speeding-up one-to-one, one-to-several, and one-to-all traffic patterns, and by providing high network capacity for all-to-all traffic.
 BCube exhibits graceful performance degradation as the server and/or switch failure rate increases. This property is of special importance for shipping-container data centers, since once the container is sealed and operational, it becomes very difficult to repair or replace its components.
 Our implementation experiences show that BCube can be seamlessly integrated with the TCP/IP protocol stack and BCube packet forwarding can be efficiently implemented in both hardware and software. Experiments in our testbed demonstrate that BCube is fault tolerant and load balancing and it significantly accelerates representative bandwidth-intensive applications.

title: Using Proactive Fault-Tolerance Approach to Enhance Cloud Service Reliability
abstract: The large-scale utilization of cloud computing services for hosting industrial/enterprise applications has led to the emergence of cloud service reliability as an important issue for both cloud service providers and users. To enhance cloud service reliability, two types of fault tolerance schemes, reactive and proactive, have been proposed. Existing schemes rarely consider the problem of coordination among multiple virtual machines (VMs) that jointly complete a parallel application. Without VM coordination, the parallel application execution results will be incorrect. To overcome this problem, we first propose an initial virtual cluster allocation algorithm according to the VM characteristics to reduce the total network resource consumption and total energy consumption in the data center. Then, we model CPU temperature to anticipate a deteriorating physical machine (PM). We migrate VMs from a detected deteriorating PM to some optimal PMs. Finally, the selection of the optimal target PMs is modeled as an optimization problem that is solved using an improved particle swarm optimization algorithm. We evaluate our approach against five related approaches in terms of the overall transmission overhead, overall network resource consumption, and total execution time while executing a set of parallel applications. Experimental results demonstrate the efficiency and effectiveness of our approach.

title: Cloud Service Reliability Enhancement via Virtual Machine Placement Optimization
abstract: With rapid adoption of the cloud computing model, many enterprises have begun deploying cloud-based services. Failures of virtual machines (VMs) in clouds have caused serious quality assurance issues for those services. VM replication is a commonly used technique for enhancing the reliability of cloud services. However, when determining the VM redundancy strategy for a specific service, many state-of-the-art methods ignore the huge network resource consumption issue that could be experienced when the service is in failure recovery mode. This paper proposes a redundant VM placement optimization approach to enhancing the reliability of cloud services. The approach employs three algorithms. The first algorithm selects an appropriate set of VM-hosting servers from a potentially large set of candidate host servers based upon the network topology. The second algorithm determines an optimal strategy to place the primary and backup VMs on the selected host servers with k-fault-tolerance assurance. Lastly, a heuristic is used to address the task-to-VM reassignment optimization problem, which is formulated as finding a maximum weight matching in bipartite graphs. The evaluation results show that the proposed approach outperforms four other representative methods in network resource consumption in the service recovery stage.

title: Adaptive Framework for Reliable Cloud Computing Environment
abstract: Cloud computing technology has become an integral trend in the market of information technology. Cloud computing virtualization and its Internet-based lead to various types of failures to occur and thus the need for reliability and availability has become a crucial issue. To ensure cloud reliability and availability, a fault tolerance strategy should be developed and implemented. Most of the early fault tolerant strategies focused on using only one method to tolerate faults. This paper presents an adaptive framework to cope with the problem of fault tolerance in cloud computing environments. The framework employs both replication and checkpointing methods in order to obtain a reliable platform for carrying out customer requests. Also, the algorithm determines the most appropriate fault tolerance method for each selected virtual machine. Simulation experiments are carried out to evaluate the framework’s performance. The results of the experiments show that the proposed framework improves the performance of the cloud in terms of throughput, overheads, monetary cost, and availability.

title: Enhancing reliability via checkpointing in cloud computing systems
abstract: Cloud computing is becoming an important solution for providing scalable computing resources via Internet. Because there are tens of thousands of nodes in data center, the probability of server failures is nontrivial. Therefore, it is a critical challenge to guarantee the service reliability. Fault-tolerance strategies, such as checkpoint, are commonly employed. Because of the failure of the edge switches, the checkpoint image may become inaccessible. Therefore, current checkpoint-based fault tolerance method cannot achieve the best effect. In this paper, we propose an optimal checkpoint method with edge switch failure-aware. The edge switch failure-aware checkpoint method includes two algorithms. The first algorithm employs the data center topology and communication characteristic for checkpoint image storage server selection. The second algorithm employs the checkpoint image storage characteristic as well as the data center topology to select the recovery server. Simulation experiments are performed to demonstrate the effectiveness of the proposed method.

title: Elastic Reliability Optimization Through Peer-to-Peer Checkpointing in Cloud Computing
abstract: Modern day data centers coordinate hundreds of thousands of heterogeneous tasks and aim at delivering highly reliable cloud computing services. Although offering equal reliability to all users benefits everyone at the same time, users may find such an approach either inadequate or too expensive to fit their individual requirements, which may vary dramatically. In this paper, we propose a novel method for providing elastic reliability optimization in cloud computing. Our scheme makes use of peer-to-peer checkpointing and allows user reliability levels to be jointly optimized based on an assessment of their individual requirements and total available resources in the data center. We show that the joint optimization can be efficiently solved by a distributed algorithm using dual decomposition. The solution improves resource utilization and presents an additional source of revenue to data center operators. Our validation results suggest a significant improvement of reliability over existing schemes.

title: Dynamic and Fault-Tolerant Clustering for Scientific Workflows
abstract: Task clustering has proven to be an effective method to reduce execution overhead and to improve the computational granularity of scientific workflow tasks executing on distributed resources. However, a job composed of multiple tasks may have a higher risk of suffering from failures than a single task job. In this paper, we conduct a theoretical analysis of the impact of transient failures on the runtime performance of scientific workflow executions. We propose a general task failure modeling framework that uses a maximum likelihood estimation-based parameter estimation process to model workflow performance. We further propose three fault-tolerant clustering strategies to improve the runtime performance of workflow executions in faulty execution environments. Experimental results show that failures can have significant impact on executions where task clustering policies are not fault-tolerant, and that our solutions yield makespan improvements in such scenarios. In addition, we propose a dynamic task clustering strategy to optimize the workflow's makespan by dynamically adjusting the clustering granularity when failures arise. A trace-based simulation of five real workflows shows that our dynamic method is able to adapt to unexpected behaviors, and yields better makespans when compared to static methods.

title: Reliability-Based Design Optimization for Cloud Migration
abstract: The on-demand use, high scalability, and low maintenance cost nature of cloud computing have attracted more and more enterprises to migrate their legacy applications to the cloud environment. Although the cloud platform itself promises high reliability, ensuring high quality of service is still one of the major concerns, since the enterprise applications are usually complicated and consist of a large number of distributed components. Thus, improving the reliability of an application during cloud migration is a challenging and critical research problem. To address this problem, we propose a reliability-based optimization framework, named ROCloud, to improve the application reliability by fault tolerance. ROCloud includes two ranking algorithms. The first algorithm ranks components for the applications that all their components will be migrated to the cloud. The second algorithm ranks components for hybrid applications that only part of their components are migrated to the cloud. Both algorithms employ the application structure information as well as the historical reliability information for component ranking. Based on the ranking result, optimal fault-tolerant strategy will be selected automatically for the most significant components with respect to their predefined constraints. The experimental results show that by refactoring a small number of error-prone components and tolerating faults of the most significant components, the reliability of the application can be greatly improved.

title: Secure Distributed Deduplication Systems with Improved Reliability
abstract: Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. However, there is only one copy for each file stored in cloud even if such a file is owned by a huge number of users. As a result, deduplication system improves storage utilization while reducing reliability. Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced by users to cloud. Aiming to address the above security challenges, this paper makes the first attempt to formalize the notion of distributed reliable deduplication system. We propose new distributed deduplication systems with higher reliability in which the data chunks are distributed across multiple cloud servers. The security requirements of data confidentiality and tag consistency are also achieved by introducing a deterministic secret sharing scheme in distributed storage systems, instead of using convergent encryption as in previous deduplication systems. Security analysis demonstrates that our deduplication systems are secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement the proposed systems and demonstrate that the incurred overhead is very limited in realistic environments.

title: Using Imbalance Characteristic for Fault-Tolerant Workflow Scheduling in Cloud Systems
abstract: Resubmission and replication are two fundamental and widely recognized techniques in distributed computing systems for fault tolerance. The resubmission based strategy has an advantage in resource utilization, while the replication based strategy can reduce the task completed time in the context of fault. However, few researches take these two techniques together for fault-tolerant workflow scheduling, especially in Cloud systems. In this paper, we present a novel fault-tolerant workflow scheduling (ICFWS) algorithm for Cloud systems by combining the aforementioned two strategies together to play their respective advantages for fault tolerance while trying to meet the soft deadline of workflow. First, it divides the soft deadline of workflow into multiple sub-deadlines for all tasks. Then, it selects a reasonable fault-tolerant strategy and reserves suitable resource for each task by taking the imbalance sub-deadlines among tasks and on-demand resource provisioning of Cloud systems into consideration. Finally, an online scheduling and reservation adjustment scheme is designed to select a suitable resource for the task with resubmission strategy and adjust the sub-deadlines as well as fault-tolerant strategies of some unexecuted tasks during the task execution process, respectively. The proposed algorithm is evaluated on both real-world and randomly generated workflows. The results demonstrate that the ICFWS outperforms some well-known approaches on corresponding metrics.

title: NUMARCK: Machine Learning Algorithm for Resiliency and Checkpointing
abstract: Data check pointing is an important fault tolerance technique in High Performance Computing (HPC) systems. As the HPC systems move towards exascale, the storage space and time costs of check pointing threaten to overwhelm not only the simulation but also the post-simulation data analysis. One common practice to address this problem is to apply compression algorithms to reduce the data size. However, traditional lossless compression techniques that look for repeated patterns are ineffective for scientific data in which high-precision data is used and hence common patterns are rare to find. This paper exploits the fact that in many scientific applications, the relative changes in data values from one simulation iteration to the next are not very significantly different from each other. Thus, capturing the distribution of relative changes in data instead of storing the data itself allows us to incorporate the temporal dimension of the data and learn the evolving distribution of the changes. We show that an order of magnitude data reduction becomes achievable within guaranteed user-defined error bounds for each data point. We propose NUMARCK, North western University Machine learning Algorithm for Resiliency and Check pointing, that makes use of the emerging distributions of data changes between consecutive simulation iterations and encodes them into an indexing space that can be concisely represented. We evaluate NUMARCK using two production scientific simulations, FLASH and CMIP5, and demonstrate a superior performance in terms of compression ratio and compression accuracy. More importantly, our algorithm allows users to specify the maximum tolerable error on a per point basis, while compressing the data by an order of magnitude.

title: A Brief Survey of Machine Learning Methods for Classification in Networked Data and an Application to Suspicion Scoring
abstract: This paper surveys work from the field of machine learning on the problem of within-network learning and inference. To give motivation and context to the rest of the survey, we start by presenting some (published) applications of within-network inference. After a brief formulation of this problem and a discussion of probabilistic inference in arbitrary networks, we survey machine learning work applied to networked data, along with some important predecessors--mostly from the statistics and pattern recognition literature. We then describe an application of within-network inference in the domain of suspicion scoring in social networks. We close the paper with pointers to toolkits and benchmark data sets used in machine learning research on classification in network data. We hope that such a survey will be a useful resource to workshop participants, and perhaps will be complemented by others.

title: Fault-tolerant control algorithm of neural network based on Particle Swarm Optimization
abstract: A fault-tolerant control method combining fault diagnosis and fault-tolerant control is proposed for sensor faults. A BP neural network based on Particle Swarm Optimization algorithm is used to estimate system states and fault parameters of the constructed model for sensor faults. The estimated fault parameters are processed by the modified Bayes classification algorithm to achieve sensor faults diagnosis, separation and estimation on-line, and sensor faults are described as "equivalent bias" vectors to realize fault-tolerant control by compensation algorithm. Simulation results for continuous stirred tank reactor (CSTR) show good convergence of the approach and strong fault-tolerant ability for sensor faults.

title: Multi-objective Meta-heuristics for Scheduling Applications with High Availability Requirements and Cost Constraints in Multi-Cloud Environments
abstract: As the popularity of cloud computing increases, more and more applications are migrated onto them. Web 2.0 applications are the most common example of such applications. These applications require to scale, be highly available, fault tolerant and able to run uninterrupted for long periods of time (or even indefinitely). Moreover as new cloud providers appear there is a natural tendency towards choosing the best provider or a combination of them for deploying the application. Thus multi-cloud scenarios emerge from this situation. However, as multi-cloud resource provisioning is both complex and costly, the choice of which resources to lend and how to allocate them to application components needs to rely on efficient strategies. These need to take into account many factors including deployment and run-time cost, resource load, and application availability in case of failures. For this aim multi-objective scheduling algorithms seem an appropriate choice. This paper presents an algorithm which tries to achieve application high-availability and fault-tolerance while reducing the application cost and keeping the resource load maximized. The proposed algorithm is compared with a classic Round Robin strategy - used by many commercial clouds - and the obtained results prove the efficiency of our solution.

title: Metaheuristics in combinatorial optimization: Overview and conceptual comparison
abstract: The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

title: A survey on metaheuristics for stochastic combinatorial optimization
abstract: Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this field.

title: Machine Learning Based DDoS Attack Detection from Source Side in Cloud
abstract: Denial of service (DOS) attacks are a serious threat to network security. These attacks are often sourced from virtual machines in the cloud, rather than from the attacker's own machine, to achieve anonymity and higher network bandwidth. Past research focused on analyzing traffic on the destination (victim's) side with predefined thresholds. These approaches have significant disadvantages. They are only passive defenses after the attack, they cannot use the outbound statistical features of attacks, and it is hard to trace back to the attacker with these approaches. In this paper, we propose a DOS attack detection system on the source side in the cloud, based on machine learning techniques. This system leverages statistical information from both the cloud server's hypervisor and the virtual machines, to prevent network packages from being sent out to the outside network. We evaluate nine machine learning algorithms and carefully compare their performance. Our experimental results show that more than 99.7% of four kinds of DOS attacks are successfully detected. Our approach does not degrade performance and can be easily extended to broader DOS attacks.

title: An advanced reinforcement learning approach for energy-aware virtual machine consolidation in cloud data centers
abstract: Energy awareness presents an immense challenge for cloud computing infrastructure and the development of next generation data centers. Inefficient resource utilization is one of the greatest causes of energy consumption in data center operations. To address this problem we introduce an Advanced Reinforcement Learning Consolidation Agent (ARLCA) capable of optimizing the distribution of virtual machines across the data center for improved resource management. Determining efficient policies in dynamic environments can be a difficult task, however the proposed Reinforcement Learning (RL) approach learns optimal behaviour in the absence of complete knowledge due to its innate ability to reason under uncertainty. Using real workload data we evaluate our algorithm against a state-of-the-art heuristic, our model shows a significant improvement in energy consumption while also reducing the number of service violations.

title: A fault tolerant router with optimized an colony algorithm
abstract: Network on Chip (NOC) approach has appeared as a solution for on-chip communications to allow integrating various processors and on-chip memories into a single chip. During the field operation of NOC, the latent hard faults technologically advanced in First In First Out buffers (FIFO). By using on line test techniques, that faults have been spotted. In this technique, the test has to be repeated occasionally to avoid the gathering of faults. Also design router architecture with shared queues (RoShaQ) among input ports which exploits the buffer operation by permitting the sharing multiple buffer queues. The efficient use of shared buffers achieves highest possible throughput when the network load becomes heavy. when the lighter traffic load can achieves low latency by allowing packets to effectively bypass these shared queues. For load balancing in faulty networks the ant colony optimization-based fault-aware routing (ACO-FAR) algorithm is propose. The best probabilistic technique is an ant colony optimization algorithm (ACO) for solving computational problems. This technique can be reduced to result a good and shortest paths through graphs. For responding an obstacle implements the three mechanisms as: 1) fault notification; 2) search the path; and 3) select the path.

title: Ant Colony Optimization Based Service Flow Scheduling with Various QoS Requirements in Cloud Computing
abstract: There are a mass of researches on the issue of scheduling in cloud computing, most of them, however, are bout workflow and job scheduling. There are fewer researches on service flow scheduling. Here we propose a model of service flow scheduling with various quality of service (QoS) requirements in cloud computing firstly, then we adopt the use of an ant colony optimization (ACO) algorithm to optimize service flow scheduling. In our model, we use default rate to describe the radio of cloud service provider breaking service level agreement (SLA), and also introduce an SLA monitoring module to monitor the running state of cloud services.

title: A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments
abstract: Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the ‘execution time’. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing ‘Best Resource Selection’ (BRS) algorithm. Our results show that PSO can achieve: a) as much as 3 times cost savings as compared to BRS, and b) good distribution of workload onto resources.

title: Virtual Machine Scheduling Management on Cloud Computing Using Artificial Bee Colony
abstract: Resource scheduling management design on Cloud computing is an important problem. Scheduling model, cost, quality of service, time, and conditions of the request for access to services are factors to be focused. A good task scheduler should adapt its scheduling strategy to the changing environment and load balancing Cloud task scheduling policy. Therefore, in this paper, Artificial Bee Colony (ABC) is applied to optimize the scheduling of Virtual Machine (VM) on Cloud computing. The main contribution of work is to analyze the difference of VM load balancing algorithm and to reduce the makespan of data processing time. The scheduling strategy was simulated using CloudSim tools. Experimental results indicated that the combination of the proposed ABC algorithm, scheduling based on the size of tasks, and the Longest Job First (LJF) scheduling algorithm performed a good performance scheduling strategy in changing environment and balancing work load which can reduce the makespan of data processing time.

title: Data Center Networks: Topologies, Architectures and Fault-Tolerance Characteristics
abstract: This SpringerBrief presents a survey of data center network designs and topologies and compares several properties in order to highlight their advantages and disadvantages. The brief also explores several routing protocols designed for these topologies and compares the basic algorithms to establish connections, the techniques used to gain better performance, and the mechanisms for fault-tolerance. Readers will be equipped to understand how current research on data center networks enables the design of future architectures that can improve performance and dependability of data centers. This concise brief is designed for researchers and practitioners working on data center networks, comparative topologies, fault tolerance routing, and data center management systems. The context provided and information on future directions will also prove valuable for students interested in these topics.

title: An adaptive Cuckoo search algorithm for optimisation
abstract: Cuckoo search is one of many nature-inspired algorithms used extensively to solve optimisation problems in different fields of engineering. It is a very effective in solving global optimisation because it is able to maintain balance between local and global random walks using switching parameter. The switching parameter for the original Cuckoo search algorithm is fixed at 25% and not enough studies have been done to assess the impact of dynamic switching parameter on the performance of Cuckoo search algorithm. This paper’s contribution is the development of three new Cuckoo search algorithms based on dynamically increasing switching parameters. The three new Cuckoo search algorithms are validated on ten mathematical test functions and their results compared to those of Cuckoo search algorithms with constant and dynamically decreasing switching parameters respectively. Finally, the simulations in this study indicate that, the Cuckoo search algorithm with exponentially increasing switching parameter outperformed the other Cuckoo search algorithms.

title: Firefly Algorithm: Recent Advances and Applications
abstract: Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higherdimensional optimisation problems.

title: Dynamic and Adaptive Fault Tolerant Scheduling With QoS Consideration in Computational Grid
abstract: Provisioning fault-tolerant scheduling in computational grid is a challenging task. Most of the existing fault tolerant scheduling schemes are either geared toward proactive or reactive. Proactive schemes emphasize on the reasons responsible for generating faults, whereas reactive mechanisms come into effect after failure detection. Unlike most existing mechanisms, we present a novel, dynamic, adaptive, and hybrid fault-tolerant scheduling scheme based on proactive and reactive approaches. In the proactive approach, the resource filtration algorithm picks resources based on resource location, availability, and reliability. Unlike most existing schemes, which rely on remotely connected resources, the proposed algorithm prefers to employ locally available resources as they might have less failure tendency. To cope with the frequent turnover problem, the proposed scheme calculates resource availability time based on various newly identified parameters (e.g., mean time between availability) and picks highly available nodes for task execution. Resource reliability is an indispensable consideration in the proposed scheme and is calculated based on parameters such as jobs success or failure ratio and the types of failures encountered. We employ an optimal resource identification algorithm to determine and select optimal resources for job execution. The performance of the proposed scheme is validated through the GridSim toolkit. Compared with contemporary approaches, experimental results demonstrate the effectiveness and efficiency of the proposed scheme in terms of various performance metrics, such as wall clock time, throughput, waiting and turnaround time, number of checkpoints, and energy consumption.

title: Load balancing in cloud computing using genetic algorithm
abstract: Cloud-computing is an enticing topic; it developed very quickly. Numerous huge cloud organizations, alike as Amazon, Yahoo, Google, offer many cloud-services and have many users. Cloud-computing is an Internet-based computing approach, where the software, resources and the applications are shared between many-to-many computing devices. Cloud Load-balancing (CLB) takes the wealth of the cloud's scalability and the physically to meet rerouted workload and to improve overall availability. In the addition of tasks goods and traffic distribution, CLB technology provides fitness checks to the cloud applications. We used GA approach to handling the LB in cloud-computing. Our proposed work is more appropriate than the current techniques work, as we executed the cloudlets in less time and performing the load-balancing in more profitability.

title: Genetic-Based Task Scheduling Algorithm in Cloud Computing Environment
abstract: Nowadays, Cloud computing is widely used in companies and enterprises. However, there are some challenges in using Cloud computing. The main challenge is resource management, where Cloud computing provides IT resources (e.g., CPU, Memory, Network, Storage, etc.) based on virtualization concept and pay-as-you-go principle. The management of these resources has been a topic of much research. In this paper, a task scheduling algorithm based on Genetic Algorithm (GA) has been introduced for allocating and executing an application’s tasks. The aim of this proposed algorithm is to minimize the completion time and cost of tasks, and maximize resource utilization. The performance of this proposed algorithm has been evaluated using CloudSim toolkit.

title: Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm
abstract: The objective of this study is to optimize task scheduling and resource allocation using an improved differential evolution algorithm (IDEA) based on the proposed cost and time models on cloud computing environment.  The proposed IDEA combines the Taguchi method and a differential evolution algorithm (DEA). The DEA has a powerful global exploration capability on macro-space and uses fewer control parameters. The systematic reasoning ability of the Taguchi method is used to exploit the better individuals on micro-space to be potential offspring. Therefore, the proposed IDEA is well enhanced and balanced on exploration and exploitation. The proposed cost model includes the processing and receiving cost. In addition, the time model incorporates receiving, processing, and waiting time. The multi-objective optimization approach, which is the non-dominated sorting technique, not with normalized single-objective method, is applied to find the Pareto front of total cost and makespan.  In the five-task five-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.368 and C(IDEA, NSGA-II) of 0.3 are superior to the ratios C(DEA, IDEA) of 0.249 and C(NSGA-II, IDEA) of 0.288, respectively. In the ten-task ten-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.506 and C(IDEA, NSGA-II) of 0.701 are superior to the ratios C(DEA, IDEA) of 0.286 and C(NSGA-II, IDEA) of 0.052, respectively. Wilcoxon matched-pairs signed-rank test confirms there is a significant difference between IDEA and the other methods. In summary, the above experimental results confirm that the IDEA outperforms both the DEA and NSGA-II in finding the better Pareto-optimal solutions.  In the study, the IDEA shows its effectiveness to optimize task scheduling and resource allocation compared with both the DEA and the NSGA-II. Moreover, for decision makers, the Gantt charts of task scheduling in terms of having smaller makespan, cost, and both can be selected to make their decision when conflicting objectives are present.

title: Fault-tolerant Workflow Scheduling using Spot Instances on Clouds
abstract: Scientific workflows are used to model applications of high throughput computation and complex large scale data analysis. In recent years, Cloud computing is fast evolving as the target platform for such applications among researchers. Furthermore, new pricing models have been pioneered by Cloud providers that allow users to provision resources and to use them in an efficient manner with significant cost reductions. In this paper, we propose a scheduling algorithm that schedules tasks on Cloud resources using two different pricing models (spot and on-demand instances) to reduce the cost of execution whilst meeting the workflow deadline. The proposed algorithm is fault tolerant against the premature termination of spot instances and also robust against performance variations of Cloud resources. Experimental results demonstrate that our heuristic reduces up to 70% execution cost as against using only on-demand instances.

title: Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a Cloud computing environment
abstract: As Cloud computing is reforming the infrastructure of IT industries, it has become one of the critical security concerns of the defensive mechanisms applied to secure Cloud environment. Even if there are tremendous advancements in defense systems regarding the confidentiality, authentication and access control, there is still a challenge to provide security against availability of associated resources. Denial-of-service (DoS) attack and distributed denial-of-service (DDoS) attack can primarily compromise availability of the system services and can be easily started by using various tools, leading to financial damage or affecting the reputation. These attacks are very difficult to detect and filter, since packets that cause the attack are very much similar to legitimate traffic. DoS attack is considered as the biggest threat to IT industry, and intensity, size and frequency of the attack are observed to be increasing every year. Therefore, there is a need for stronger and universal method to impede these attacks. In this paper, we present an overview of DoS attack and distributed DoS attack that can be carried out in Cloud environment and possible defensive mechanisms, tools and devices. In addition, we discuss many open issues and challenges in defending Cloud environment against DoS attack. This provides better understanding of the DDoS attack problem in Cloud computing environment, current solution space, and future research scope to deal with such attacks efficiently.

title: Distributed Denial of Service(DDoS) Attack Techniques and Prevention on Cloud Environment
abstract: Cloud computing has been considered as one of the great importance and emerging networking technology, which has been changed the era of computing in last few years. Despite the security concerns of protecting data or providing continuous service over the cloud, many organisations are considering different types cloud services as potential solution for their business. We are researching on cloud computing security issues for cloud service providers. Researchers have demonstrated that the extremely important issues of Distributed Denial of Service (DDoS) attack and defense are the resource competition between the attackers and defenders. A cloud usually have profound resources and has full control and dynamic allocation capabilities. Therefore, cloud offers us the potential to overcome DDoS Attack. We propose a cloud-enabled defense mechanism for Internet services against DDoS attacks.

title: Cloud-based DDoS attacks and defenses
abstract: Safety and reliability are important in the cloud computing environment. This is especially true today as distributed denial-of-service (DDoS) attacks constitute one of the largest threats faced by Internet users and cloud computing services. DDoS attacks target the resources of these services, lowering their ability to provide optimum usage of the network infrastructure. Due to the nature of cloud computing, the methodologies for preventing or stopping DDoS attacks are quite different compared to those used in traditional networks. In this paper, we investigate the effect of DDoS attacks on cloud resources and recommend practical defense mechanisms against different types of DDoS attacks in the cloud environment.

title: Botnet-based Distributed Denial of Service (DDoS) Attacks on Web Servers: Classification and Art
abstract: Botnets are prevailing mechanisms for the facilitation of the distributed denial of service (DDoS) attacks on computer networks or applications. Currently, Botnet-based DDoS attacks on the application layer are latest and most problematic trends in network security threats. Botnet-based DDoS attacks on the application layer limits resources, curtails revenue, and yields customer dissatisfaction, among others. DDoS attacks are among the most difficult problems to resolve online, especially, when the target is the Web server. In this paper, we present a comprehensive study to show the danger of Botnet-based DDoS attacks on application layer, especially on the Web server and the increased incidents of such attacks that has evidently increased recently. Botnet-based DDoS attacks incidents and revenue losses of famous companies and government websites are also described. This provides better understanding of the problem, current solution space, and future research scope to defend against such attacks efficiently.

title: Auditing Defense against XSS Worms in Online Social Network-Based Web Applications
abstract: Nowadays, users of Online Social Network (OSN) are less familiar with cyber security threats that occur in such networks, comprising Cross-Site Scripting (XSS) worms, Distributed Denial of Service (DDoS) attacks, Phishing, etc. Numerous defensive methodologies exist for mitigating the effect of DDoS attacks and Phishing vulnerabilities from OSN. However, till now, no such robust defensive solution is proposed for the complete alleviation of XSS worms from such networks. This chapter discusses the detailed incidences of XSS attacks in the recent period on the platforms of OSN. A high level of taxonomy of XSS worms is illustrated in this article for the precise interpretation of its exploitation in multiple applications of OSN like Facebook, Twitter, LinkedIn, etc. We have also discussed the key contributions of current defensive solutions of XSS attacks on the existing platforms of OSN. Based on this study, we identified the current performance issues in these existing solutions and recommend future research guidelines.

title: XSS-secure as a service for the platforms of online social network-based multimedia web applications in cloud
abstract: This article presents a novel framework XSS-Secure, which detects and alleviates the propagation of Cross-Site Scripting (XSS) worms from the Online Social Network (OSN)-based multimedia web applications on the cloud environment. It operates in two modes: training and detection mode. The former mode sanitizes the extracted untrusted variables of JavaScript code in a context-aware manner. This mode stores such sanitized code in sanitizer snapshot repository and OSN web server for further instrumentation in the detection mode. The detection mode compares the sanitized HTTP response (HRES) generated at the OSN web server with the sanitized response stored at the sanitizer snapshot repository. Any variation observed in this HRES message will indicate the injection of XSS worms from the remote OSN servers. XSS-Secure determines the context of such worms, perform the context-aware sanitization on them and finally sanitized HRES is transmitted to the OSN user. The prototype of our framework was developed in Java and integrated its components on the virtual machines of cloud environment. The detection and alleviation capability of our cloud-based framework was tested on the platforms of real world multimedia-based web applications including the OSN-based Web applications. Experimental outcomes reveal that our framework is capable enough to mitigate the dissemination of XSS worm from the platforms of non-OSN Web applications as well as OSN web sites with acceptable false negative and false positive rate.

title: WorkflowSim: A toolkit for simulating scientific workflows in distributed environments
abstract: Simulation is one of the most popular evaluation methods in scientific workflow studies. However, existing workflow simulators fail to provide a framework that takes into consideration heterogeneous system overheads and failures. They also lack the support for widely used workflow optimization techniques such as task clustering. In this paper, we introduce WorkflowSim, which extends the existing CloudSim simulator by providing a higher layer of workflow management. We also indicate that to ignore system overheads and failures in simulating scientific workflows could cause significant inaccuracies in the predicted workflow runtime. To further validate its value in promoting other research work, we introduce two promising research areas for which WorkflowSim provides a unique and effective evaluation platform.

title: A Survey on Mobile Data Offloading Technologies
abstract: Recently, due to the increasing popularity of enjoying various multimedia services on mobile devices (e.g., smartphones, ipads, and electronic tablets), the generated mobile data traffic has been explosively growing and has become a serve burden on mobile network operators. To address such a serious challenge in mobile networks, an effective approach is to manage data traffic by using complementary technologies (e.g., small cell network, WiFi network, and so on) to achieve mobile data offloading. In this paper, we discuss the recent advances in the techniques of mobile data offloading. Particularly, based on the initiator diversity of data offloading, we classify the existing mobile data offloading technologies into four categories, i.e., data offloading through small cell networks, data offloading through WiFi networks, data offloading through opportunistic mobile networks, and data offloading through heterogeneous networks. Besides, we show a detailed taxonomy of the related mobile data offloading technologies by discussing the pros and cons for various offloading technologies for different problems in mobile networks. Finally, we outline some opening research issues and challenges, which can provide guidelines for future research work.

title: Energy-efficient fault tolerance approach for Internet of Things applications
abstract: Fault tolerance (FT) is essential in many Internet of Things (IoT) applications, in particular in the domains such as medical devices and automotive systems where a single fault in the system can lead to serious consequences. Non-volatile memory (NVM), on the other hand, is commonly used to improve system reliability due to its unique properties to retain data even if the power supply is lost. However, one of the most important drawbacks of NVM is that it imposes significant overhead regarding timing and energy. In this paper, we have proposed a unique technique with the use of NVM to create FT application specific architecture with almost no timing overhead and low energy overhead. We address the implementation of applications that are specified using synchronous data flow model of computation. We combine the use of NVM and classical CMOS transistors so that NVM judiciously stores selected complete states of the pertinent program. It allows the program to resume from the saved state in NVM when faults occur. The frequency of the state selection can be flexibly adjusted for an arbitrarily specified FT timing/energy overhead. Moreover, to find an optimal state selection (with low overhead), we have applied an improved min-cut max-flow algorithm. On a variety of typical benchmarks, the simulation results indicate that our approach incurs only a small overhead over lower bounds. It is also generic in a sense that it can be applied to a wide spectrum of underlying IoT architectures and computational models.

title: Social Cloud: Cloud Computing in Social Networks
abstract: With the increasingly ubiquitous nature of Social networks and Cloud computing, users are starting to explore new ways to interact with, and exploit these developing paradigms. Social networks are used to reflect real world relationships that allow users to share information and form connections between one another, essentially creating dynamic Virtual Organizations. We propose leveraging the pre-established trust formed through friend relationships within a Social network to form a dynamic“Social Cloud”, enabling friends to share resources within the context of a Social network. We believe that combining trust relationships with suitable incentive mechanisms (through financial payments or bartering) could provide much more sustainable resource sharing mechanisms. This paper outlines our vision of, and experiences with, creating a Social Storage Cloud, looking specifically at possible market mechanisms that could be used to create a dynamic Cloud infrastructure in a Social network environment.

title: Social Networking for Sharing Cloud Resources
abstract: Cloud computing is a model for enabling convenient, ubiquitous and on-demand network access to a shared pool of configurable computing resources (e.g. storage, applications, and networks) that can be provisioned with minimal management effort. Despite all these benefits, the sharing of resources with other users is a challenge, cloud providers do not commonly facilitate users in sharing their dedicated resources with others. In developing countries it is often too expensive for people to acquire a virtual machine of their own. Users may therefore wish to manage costs and increase computational resource usage by sharing their instances with others. Sadly it is not easy to do this at present. Social networks provide a structure that allows users to interact and share resources (e.g. pictures and videos) on the basis of a trustworthy relationship (e.g. Friendship). This paper highlights a Cloud Resource Bartering model (CRB-model) for sharing user's computational resources through a social network. In our approach we have linked a social network with the computational cloud to create a social cloud (SC) so that users can share their part of the cloud with their social community. A prototype system has been deployed on a social network by using the bartering resource trading mechanism. It is anticipated that this may help users to share their dedicated resources without the need for money changing hands and different communities.

title: A deep learning approach for VM workload prediction in the cloud
abstract: In order to manage the resources in cloud efficiently, ensure the performance of cloud services and reduce the power consumption, it is critical to predict the workload of virtual machines (VM) accurately. In this paper, a new approach for VM workload prediction based on deep learning was proposed. A deep learning prediction model was designed with a deep belief network (DBN) composed of multiple-layered restricted Boltzmann machines (RBMs) and a regression layer. The DBN is used to extract the high level features from all VMs workload data and the regression layer is used to predict the workload of the VMs in the future. With little prior knowledge, DBN could learn the features efficiently for the VM workload prediction in an unsupervised fashion. Experimental results show that the proposed approach improves the workload prediction performance compared with other widely used workload prediction approaches.

title: DRL-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers
abstract: Cloud computing has become an attractive computing paradigm in both academia and industry. Through virtualization technology, Cloud Service Providers (CSPs) that own data centers can structure physical servers into Virtual Machines (VMs) to provide services, resources, and infrastructures to users. Profit-driven CSPs charge users for service access and VM rental, and reduce power consumption and electric bills so as to increase profit margin. The key challenge faced by CSPs is data center energy cost minimization. Prior works proposed various algorithms to reduce energy cost through Resource Provisioning (RP) and/or Task Scheduling (TS). However, they have scalability issues or do not consider TS with task dependencies, which is a crucial factor that ensures correct parallel execution of tasks. This paper presents DRL-Cloud, a novel Deep Reinforcement Learning (DRL)-based RP and TS system, to minimize energy cost for large-scale CSPs with very large number of servers that receive enormous numbers of user requests per day. A deep Q-learning-based two-stage RP-TS processor is designed to automatically generate the best long-term decisions by learning from the changing environment such as user request patterns and realistic electric price. With training techniques such as target network, experience replay, and exploration and exploitation, the proposed DRL-Cloud achieves remarkably high energy cost efficiency, low reject rate as well as low runtime with fast convergence. Compared with one of the state-of-the-art energy efficient algorithms, the proposed DRL-Cloud achieves up to 320% energy cost efficiency improvement while maintaining lower reject rate on average. For an example CSP setup with 5,000 servers and 200,000 tasks, compared to a fast round-robin baseline, the proposed DRL-Cloud achieves up to 144% runtime reduction.

title: Security Implications of Blockchain Cloud with Analysis of Block Withholding Attack
abstract: The blockchain technology has emerged as an attractive solution to address performance and security issues in distributed systems. Blockchain's public and distributed peer-to-peer ledger capability benefits cloud computing services which require functions such as, assured data provenance, auditing, management of digital assets, and distributed consensus. Blockchain's underlying consensus mechanism allows to build a tamper-proof environment, where transactions on any digital assets are verified by set of authentic participants or miners. With use of strong cryptographic methods, blocks of transactions are chained together to enable immutability on the records. However, achieving consensus demands computational power from the miners in exchange of handsome reward. Therefore, greedy miners always try to exploit the system by augmenting their mining power. In this paper, we first discuss blockchain's capability in providing assured data provenance in cloud and present vulnerabilities in blockchain cloud. We model the block withholding (BWH) attack in a blockchain cloud considering distinct pool reward mechanisms. BWH attack provides rogue miner ample resources in the blockchain cloud for disrupting honest miners' mining efforts, which was verified through simulations.

title: Blockchain Security in Cloud Computing: Use Cases, Challenges, and Solutions
abstract: Blockchain has drawn attention as the next-generation financial technology due to its security that suits the informatization era. In particular, it provides security through the authentication of peers that share virtual cash, encryption, and the generation of hash value. According to the global financial industry, the market for security-based blockchain technology is expected to grow to about USD 20 billion by 2020. In addition, blockchain can be applied beyond the Internet of Things (IoT) environment; its applications are expected to expand. Cloud computing has been dramatically adopted in all IT environments for its efficiency and availability. In this paper, we discuss the concept of blockchain technology and its hot research trends. In addition, we will study how to adapt blockchain security to cloud computing and its secure solutions in detail.

title: Intelligent Resource Management in Blockchain-Based Cloud Datacenters
abstract: Nowadays, more and more companies migrate business from their own servers to the cloud. With the influx of computational requests, datacenters consume tremendous energy every day, attracting great attention in the energy efficiency dilemma. In this paper, we investigate the energy-aware resource management problem in cloud datacenters, where green energy with unpredictable capacity is connected. Via proposing a robust blockchain-based decentralized resource management framework, we save the energy consumed by the request scheduler. Moreover, we propose a reinforcement learning method embedded in a smart contract to further minimize the energy cost. Because the reinforcement learning method is informed from the historical knowledge, it relies on no request arrival and energy supply. Experimental results on Google cluster traces and real-world electricity price show that our approach is able to reduce the datacenters cost significantly compared with other benchmark algorithms.