With an increase in the diversity and complexity of requirements from organizations for cloud computing, there is a growing need for integrating the services of multiple cloud providers. In such multi-cloud systems, data leakage is considered to be a major security concern, which is caused by illegitimate actions of malicious users often acting in collusion. The possibility of data leakage in such environments is characterized by the number of interoperations as well as the trustworthiness of users on the collaborating clouds. In this paper, we address the problem of secure multi-cloud collaboration from an Attribute-based Access Control (ABAC) policy management perspective. In particular, we define a problem that aims to formulate ABAC policy rules for establishing a high degree of inter-cloud accesses while eliminating potential paths for data leakage. A data leakage free ABAC policy generation algorithm is proposed that first determines the likelihood of data leakage and then attempts to maximize inter-cloud collaborations. Experimental results on several large data sets show the efficacy of the proposed approach.

Vehicular ad-hoc networks formed in an Internet of Vehicles scenario can enable many useful applications and services. Many of these applications may generate a large amount of data, which needs to be processed within some deadline to be useful. Limited resources present in vehicles may not be sufficient for processing such data. The resources present in Road Side Units (RSUs) can be used for this purpose by running Virtual Machines (VMs) there on behalf of the vehicles. However, RSUs can also become overloaded in a dense vehicular scenario if all vehicles use the services of their nearby RSUs only. Also, use of RSUs may incur a cost. Hence the combined total resources of the RSUs need to be carefully managed to ensure that a large number of VMs complete within their deadline while incurring a lower cost. In this paper, we propose an algorithm called Cost Aware Load Balancing (CALB) algorithm that assigns and executes VMs in different RSUs in the total RSU pool. The proposed algorithm aims to maximize the number of VMs that complete execution within their deadline and also attempts to minimize the overall cost incurred by VMs for using RSUs’ resources. Performance of CALB is compared with several existing algorithms to show that it works better than the existing algorithms with respect to several performance metrics.

The growing interest in Vehicular Ad hoc NETworks (VANETs) has introduced a large number of vehicular applications. Many such applications generate data that needs to be processed. Virtual machines (VMs) created in Road Side Units (RSUs) with some storage and computing resources can be used to serve the application requests for data processing at the edge. However, the resources available at RSUs are limited and proper load balancing is needed to avoid overloaded RSUs and satisfy application requirements. Migrating VMs between RSUs can be a useful technique for achieving this. However, VM migrations can be costly. In this work, we propose a matching-based dynamic load balancing algorithm which aims to serve maximum number of requests from vehicles while incurring minimum migration cost. Detailed simulation results obtained using real traffic traces show that the proposed technique improves the number of applications served while reducing the migration cost.

Most existing UE association techniques consider signal strength from eNBs as the primary metric for handover decisions. This may result in unbalanced distribution of UEs to eNBs in a dense LTE network with large number of users, even though a UE may have multiple good eNB options for association. In this paper we propose a load balancing framework that can be applied over any existing handover algorithm periodically to balance eNB loads. The framework uses a novel metric based on eNB-availability-option of UEs for choosing the set of UEs to handover to balance load and their target eNBs, while ensuring good signal quality. A specific algorithm based on the framework is proposed and evaluated by simulating on NS-3 over five existing handover algorithms.

Many applications in an IoV setting require transfer of large data between vehicles that cannot be transferred in a single vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) contact. In this paper, we propose a scheme for large sized data transfer between vehicles by breaking up the data into smaller fragments (that can be transferred in a single contact) which are transferred to the destination using any number of other vehicles and roadside units (RSUs) as intermediate nodes. We formulate the problem as a scheduling problem and propose an offline algorithm for it. Detailed simulation results on realistic scenarios are presented to show that the use of other vehicles as intermediate nodes significantly increases the number of successful transfers.

Handover, the process of transferring a call or data session from one base station to another without disconnection, is an important problem in LTE networks. Several handover algorithms have been proposed for LTE networks in general. However, they mostly use the current signal strengths for making handover decisions, which can cause spurious handovers in a dense eNB deployment. In this paper, we investigate the use of look-ahead signal strength information for reducing spurious handovers. We first propose a novel graph-based framework that uses signal strength information along the mobile trajectory of the UE to make better handover decisions. Two algorithms are then presented based on this framework. The first algorithm assumes that exact signal measurements at the UE from all eNBs in its trajectory are available a priori for all time instances, and provides a baseline reference for finding the minimum number of handovers that can be achieved. The second algorithm uses the exact signal measurement for the current time instance only, and estimates the signal strengths for future time instances. The performances of the algorithms are compared with four existing LTE handover algorithms using simulation on real world data. It is shown that the proposed algorithms significantly reduce the number of handovers while still maintaining good signal quality for communication throughout the trajectory of the UE.

Electric Vehicles (EVs) are being increasingly seen as an eco-friendly alternative to conventional internal combustion vehicles. EVs require periodic charging of their batteries and fixed charging stations (FCSs) have been used commonly to charge EV batteries. However, FCSs may not be able to charge many EVs during increased demands. Mobile charging stations (MCS) have been proposed for charging EVs at different spots, with an MCS carrying batteries travelling to the EV to charge its batteries. In this paper, we propose a framework for temporarily increasing the capacity of FCSs by scheduling MCSs to service additional EVs during increased demand. The problem of scheduling the MCSs to different FCSs based on EV charging demands is formulated, and is shown to be NP-hard. Two offline heuristic algorithms to solve the problem are proposed. Detailed simulation on a real-world map is presented to show that the algorithms perform well in realistic scenarios.

Applications that span across multiple clouds are often found to be vulnerable to security threats. Such highly heterogeneous environments need a fine-grained access control mechanism like Attribute-based Access Control (ABAC) for enforcing security. A first step towards successfully deploying ABAC is to define an appropriate set of access control rules that establish the desired inter-cloud accesses. This becomes more challenging when the access requirements vary with time or the users and objects are updated quite frequently. We study the problem of formulation of an optimal set of ABAC rules for granting inter-cloud accesses in a dynamic environment. The problem being NP-Hard, we propose heuristic solutions. Extensive experiments on benchmark datasets show encouraging results.

Data dissemination among vehicles is an important problem in a Vehicular Ad Hoc Network (VANET). However, all existing works on data dissemination consider transferring small-sized data, where the entire data can be transferred from a roadside unit (RSU) to a vehicle when the vehicle is passing by it. In this paper, we address the problem of large data transfers among vehicles in a VANET using RSUs and vehicles with finite data storage and transfer capacity. In particular, we formulate two problems, the Maximum Successful Transfers Problem Using RSUs (R-MaxSucTrans) that aims to maximize the number of successful transfers between source and destination vehicles using only RSUs as intermediate nodes, and the Maximum Successful Transfers Problem Using RSUs and Vehicles (RV-MaxSucTrans) that aims to do the same while using both RSUs and other vehicles as intermediate nodes. Offline algorithms are proposed for the two problems. Detailed simulation results are presented to evaluate the performance of the algorithms.

A belt region is said to be k-barrier covered by a set of sensors if all paths crossing the width of the belt region intersect the sensing regions of at least k sensors. Barrier coverage can be achieved from a random initial deployment of mobile sensors by suitably relocating the sensors to form a barrier. Reducing the movement of the sensors is important in such scenarios due to the energy constraints of sensor devices. In this paper, we propose a centralized algorithm which achieves 1-barrier coverage by forming a non-linear barrier from a random initial deployment of sensors in a belt. The algorithm uses a novel idea of physical behavior of chains along with the concept of virtual force. Formation of non-linear barrier reduces the movement of the sensors needed as compared to linear barriers. Detailed simulation results are presented to show that the proposed algorithm achieves barrier coverage with less displacement of sensors compared to other existing algorithms in the literature.

Summary Security of applications has been identified as one of the major concerns in today's multicloud collaborative environment. These applications are often bounded by the constraints of the disparate cloud domains they are deployed in. A fine‐grained access control mechanism such as attribute‐based access control (ABAC) is considered to be an appropriate choice for authorization management in this context. However, identifying a suitable set of ABAC rules, often called rule mining, is a critical step in building ABAC‐based systems. We propose 2 approaches for intercloud rule formation in ABAC. In the first approach, we consider cross domain rule mining as the problem of forming a minimal set of positive authorizations only. The second approach shows the advantage of developing deny rules along with positive authorizations in reducing the total number of rules, and hence, the response time for evaluating access requests. The problem is proved to be NP‐hard. Heuristic solutions are proposed and evaluated on benchmark datasets showing encouraging results.

Security in multi-cloud collaborative environment requires a fine-grained access control mechanism. Attribute Based Access Control (ABAC) is considered to be a suitable choice in such situations. However, identification of a correct set of ABAC rules is a crucial step in establishing secure collaborations among multiple clouds. In this paper, we formally define cross-domain rule mining as the problem of finding a minimal set of ABAC rules that allow access to the resources of one cloud by the users of another cloud. The problem is shown to be NP-Hard and a heuristic algorithm is proposed to solve it. Experiments on an extensive set of benchmark and synthetic data show encouraging results.

Many future vehicular ad hoc network (VANET) applications will be event driven and will require events of different types to be delivered to moving vehicles within some specified time. In this paper, we propose a publish-subscribe based event notification framework that uses roadside units (RSUs) to deliver events to vehicles that subscribe to them within the validity periods of both the subscriptions and the events. Each RSU can disseminate only a finite number of events at a time and has a cost associated with it. Two scheduling problems to schedule the dissemination of events from RSUs are formulated. The first problem aims to maximize only the number of subscriptions that are matched to some events. The second problem, in addition to maximizing the number of subscriptions matched, also aims to minimize the total cost of disseminating the events. We have designed offline and online algorithms for the problems that a service provider can execute to schedule event disseminations from the RSUs. Detailed simulation results are presented to show that the algorithms are able to match a high percentage of subscriptions with low average event dissemination cost for some realistic city traffic scenarios.

A time-varying graph (TVG) can model useful practical scenarios such as intermittent contact between nodes. High degree nodes in such networks can act as central nodes for efficient implementation of different applications such as information dissemination. In this paper, we propose a distributed algorithm for finding a low cardinality set of high degree nodes in a time-varying graph. The algorithm efficiently exploits the overlap in coverage between nodes to reduce the size of the set and finds a small subset of all high degree nodes in the network while still maintaining almost the same coverage as the set of all high degree nodes. It also finds temporal paths from all nodes to the high degree nodes found which can be used for routing information to the nodes.

Clustering is used commonly in ad hoc networks for hierarchical routing. Size-bounded clustering has been proposed to restrict the maximum size of a cluster so that the routing load, and hence the energy drain, on the clusterhead is bounded. However, all the existing size-bounded clustering algorithms take the size bound as the number of nodes in the cluster. This approach may still result in high routing load on a clusterhead if the traffic rate of the nodes in the cluster is high. In this paper, we propose a simple metric based on average traffic rates of nodes for size-bounded clustering that reflects the actual routing load on a clusterhead. We present a distributed clustering algorithm that bounds the cluster size in terms of this metric, and also reduces the total energy spent in sending all the packets. Detailed simulation results are presented to show that the protocol performs significantly better than some other size-bounded clustering schemes.

In any vehicular adhoc network, there is always a possibility of incorrect messages being transmitted either due to faulty sensors and/or intentional malicious activities. Detecting and evicting sources of such misbehavior is an important problem. We observe that the performance of misbehavior detection schemes will depend on the application under consideration and the mobility dynamics of the detecting vehicle. Further, the underlying tradeoff in any such detection algorithm is the balance between False Positives and False Negatives; one would like to detect as many misbehaviors as possible, while at the same time ensuring that the genuine vehicles are not wrongly accused. In this work we propose and analyze (via simulations) the performance of a Misbehavior Detection Scheme (MDS) for Post Crash Notification (PCN) application. We observe that the performance of this proposed scheme is not very sensitive to the exact dynamics of the vehicle on small scales, so that slight error in estimating the dynamics of the detecting vehicle does not degrade the performance of the MDS.

One-to-many negotiation is a type of automated negotiation where one agent negotiates with multiple opponents. We consider the case of one seller agent negotiating with multiple buyer agents for the sale of a single copy of a product in an e-commerce scenario. The negotiation with each buyer agent is done by a sub-seller agent under the control of the seller agent. Influencing among these sub-seller agents in a one-to-many negotiation can result in better gain. In this paper, we propose an influencing model for one-to-many negotiation. We first introduce a new strategy for the buyer based on the keenness of the buyer to make a deal. We next propose a strategy for influencing among these sub-seller agents. We also evaluate the performance of the proposed strategy extensively to show that better gain can be achieved by influencing.

Different economic models have been applied to the resource allocation problem in grids. Contract net is one such model in which a job is announced to the resources, the resources bid for the job, and then the broker chooses one bid from the received bids based on some policy. The job is awarded to the resource with the winning bid. In this paper, we first propose a contract net based resource allocation policy for grids based on an earlier policy, that performs better than other existing policies. We next perform a scalability analysis of the policy to show that it is not scalable. Finally we present strategies to make contract net scalable in large grid resource allocation problems. We present detailed simulation results to evaluate the performances of the proposed strategies.

Different market mechanisms have been used to match resources with users in grids. In this paper, we propose two simple contract-net based resource selection policies in grids with heterogeneous resources. A detailed experimental evaluation of the policies shows that they perform better than other commonly used policies for many scenarios.

Negotiation is a process between self-interested buyers and sellers in a business transaction trying to reach an agreement on one or more issues. The outcome of the negotiation depends on several parameters such as the agents' strategies and the knowledge one agent has about the others. It is important that an agent chooses a proper negotiation strategy based on the available information in order to achieve its goal. In this paper, we first perform a comparative evaluation of commonly used negotiation strategies for bilateral single-issue negotiation and show that there is no one strategy which works well in all situations. We then present an adaptive negotiation strategy that a seller can use to increase its gain. The strategy adapts itself based on information obtained about the buyer's strategy from an ongoing negotiation process and past negotiation experiences. We present extensive simulation results to show that for a wide variety of cases, the adaptive strategy gives better performance compared to the best non-adaptive strategy found from the comparative evaluation done earlier

A connected dominating set of a graph G is a set of nodes of G such that every node in G is either in the set or is adjacent to some node in the set, and the graph induced by the elements of the set is connected. Connected dominating sets have major applications in routing in wireless ad-hoc networks. In this paper, we present a distributed self-stabilizing algorithm for finding a connected dominating set of a graph. Starting from an arbitrary initial state, the algorithm finds a connected dominating set in O(N^2) time, where N is the number of nodes. We also show detailed simulation results to indicate that in practice, the algorithm finds small-sized connected dominating sets in a short time.

The contract net protocol (CNP) is a widely used coordination mechanism in multiagent systems. It has a lot of communication overhead due to the broadcast of the task announcements. The performance of the CNP degrades drastically when the number of communicating agents and the number of tasks announced increases. Hence, it has problems of scalability. In order to overcome this limitation, an instance-based learning (IBL) mechanism is designed that uses previously stored instances in order to select a target agent. This avoids the expensive bidding process. The scheme is implemented in a simulated distributed hospital system where the CNP is used for resource sharing across hospitals. Experimental results demonstrate that with the incorporation of the IBL, the system performance improves significantly. The system is better scalable with respect to the number of tasks.

An important feature in a distributed problem solving system is that the resources of different nodes can be shared through cooperation. In this paper, the generalized partial global planning (GPGP) approach used for multiagent systems is extended by providing a coordination mechanism for resource sharing across nodes. In our framework, multiple conflicting criteria (or objectives) like quality, cost, and duration may be associated with an input task. Preference ratings expressed subjectively may be assigned to each of the criteria. Task assignment in this system, which is a multiobjective decision making problem, is important for the satisfaction of the criteria. It has to be done with imprecise information since the system is dynamic and preference ratings are specified subjectively. A technique for task assignment using the fuzzy set approach is also presented in this paper. Simulation studies for the coordination mechanism and the task assignment have been performed to demonstrate their effectiveness.

Abstract Checkpoint and rollback recovery is a well‐known technique for providing fault tolerance to long‐running distributed applications. Performance of a checkpoint and recovery protocol depends on the characteristics of the application and the system on which it runs. However, given an application and system environment, there is no easy way to identify which checkpoint and recovery protocol will be most suitable for it. Conventional approaches require implementing the application with all the protocols under consideration, running them on the desired system, and comparing their performances. This process can be very tedious and time consuming. This paper first presents the design and implementation of a simulation environment, distributed process simulation or dPSIM, which enables easy implementation and evaluation of checkpoint and recovery protocols. The tool enables the protocols to be simulated under a wide variety of application, system, and network characteristics. The paper then presents performance evaluation of five checkpoint and recovery protocols. These protocols are implemented and executed in dPSIM under different simulated application, system, and network characteristics. Copyright © 2003 John Wiley & Sons, Ltd.

. Self-stabilization provides a non-masking approach to fault tolerance. Given this fact, one would hope that in a self-stabilizing system, the amount of disruption caused by a fault is proportional to the severity of the fault. However, this is not true for many self-stabilizing systems. Our paper addresses this weakness of distributed self-stabilizing systems by introducing the notion of fault containment. Informally, a fault-containing self-stabilizing algorithm is one that contains the effects of limited transient faults while retaining the property of self-stabilization. The paper begins with a formal framework for specifying and evaluating fault-containing self-stabilizing protocols. Then, it is shown that self-stabilization and fault containment are goals that can conflict. For example, it is shown that imposing a O(1) bound on the worst case recovery time from a 1-faulty state necessitates added overhead for stabilization: for some tasks, the O(1) recovery time implies stabiliz...